esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Optimal Interpolation Method
First, the a priori MDT was Gaussian filtered to obtain the large-scale signals, with the same filter radius as the large-scale MDT. Then, this large-scale MDT was removed from the a priori MDT, and only the variance of the residual part of the MDT signal was calculated and used as the variance of the a priori MDT. In addition, the correlation radius parameter in the covariance function of Equation (8) was the key parameter in the OIM, which refers to the corresponding \(x_{0}\) and \(y_{0}\) when the result of the covariance function is zero value and is generally related to the latitude and the ocean basin [5]. Similarly, the residual part of the MDT could be used for the calculation of the correlation radius in the correlation function: the study area was divided into several subregions, in which the corresponding correlation radius was calculated. It was found that a better MDT solution could be modeled when the subregion was divided into \(8^{\circ}\times 8^{\circ}\) subregions. In each subregion, the corresponding empirical covariance was calculated based on the distance between the residual MDT grids:  
\[\mathrm{C}(\psi)=\frac{\sum A_{j}A_{k}f_{j}f_{k}}{\sum A_{j}A_{k}} \tag{10}\]where \(\psi\) is the spherical distance between grids, f is the MDT value of the grid on the spherical surface, and A is the corresponding area. If the subregions are divided by equal areas, this can be simplified to:  
\[\mathrm{C}(\psi)=\frac{\sum\mathrm{f_{i}f_{k}}}{\mathrm{n}} \tag{11}\]  
where n is the number of the groups of the grids with a spherical distance of \(\psi\), which could be split by Equation (12):  
\[\psi-\frac{\Delta\psi}{2}<\psi_{\mathrm{PQ}}<\psi+\frac{\Delta\psi}{2} \tag{12}\]  
and fit C (\(\psi\)) as an empirical covariance to a known covariance function (Equation (8)) to determine the correlation radii in zonal and meridional in each region, the results of which are shown in Figure 3. The sensitivity of the inputs in modeling should be considered [36]. The sensitivity tests to the covariance of the observations have been analyzed by [PERSON] [37], and indicated that the correlation scales obtained with different numerical models showed similar patterns. The recently released CNES-CLS18 MDT was used to estimate the variance and correlation function of the MDT in this paper. In order to evaluate the performance of the detailed signal recovery of the OIM for the MDT, the geostrophic ocean current velocity could be deduced from the MDT [38]:  
\[\left\{ \begin{array}{l}\mathrm{u_{g}}=-\frac{\gamma}{\mathrm{IR}}\frac{ \partial\mathrm{h}}{\partial\phi}\\ \mathrm{v_{g}}=\frac{\gamma}{\mathrm{IR}\cos\phi}\frac{\partial\mathrm{h}}{ \partial\lambda}\end{array} \right. \tag{13}\]  
where \(\mathrm{u_{g}}\) and \(\mathrm{v_{g}}\) represent the geostrophic ocean current velocity in the zonal and meridional direction, respectively; \(\phi\) and \(\lambda\) represent the latitude and longitude of the point, respectively; \(\gamma\) represents the normal gravity, \(\mathrm{f}=2\omega\cdot\sin\phi\) is the Coriolis force, \(\omega\) is the Earth's rotation rate, and h is the MDT.
## 1 Introduction
### Disc-cutter Geometry Model  
Due to the structure difference between Nomex honeycomb-core composite material and the metal material, a corresponding special tool must be used for machining, and the structural shape and geometric angle of the tool are greatly different from the metal cutter. Nomex honeycomb-core composites material can be machined due to the tool forms a sharp cutting edge with a small wedge angle, which is the key to the material being cut. The rotary-vibration-cutting disc-cutter model studied in this paper is shown in Figure 1. Due to the complex structure of the tool, the model was built by the CATIA V5R21 and imported into the ABAQUS, the geometric parameters of disc-cutter are shown in Table 1.  
The tool material is W18 Cr4 V, material density \(\rho\) = 7900 kg/m\({}^{3}\), elasticity modulus \(E\) = 205 Gpa, Poisson's ratio \(\mu\) = 0.3. Due to the low hardness of the Nomex honeycomb-core material, the tool wear is neglected in the simulation, and the tool is defined as a rigid body.
# Inverse Kinematics of a Serial Manipulator with a Free Joint for Aerial Manipulation
## 1 Introduction
Many other advanced control approaches are currently under development to address the complex challenges posed by aerial manipulators. Among these, optimization-based techniques like Non-linear Model Predictive Control (nMPC) [16; 17] have been employed to handle the nonlinear dynamics of these systems. Also non-linear [18], adaptive [19], and Reinforcement-Learning-based [20] controllers have been applied to this field with good results. These methods offer improved performance but involve significantly higher computational complexity. In addition to control strategies, mechanical design modifications have been investigated to mitigate the dynamic coupling effect. These include strategies such as counter-balancing mechanisms [21; 22; 23], balanced manipulators [24; 25], and the use of compliant or passive elements to decouple the UAV base from the manipulator [26]. Also in this paper, a mechanical approach is exploited to reduce the dynamic coupling effect between a serial manipulator and a UAV. Specifically, we consider a non-actuated revolute joint between the UAV and the manipulator, which reduces the torque transmitted from the manipulator to the UAV, enhancing the stability of the system. Typically, inverse kinematics methods based on differential kinematics cannot be used in the presence of a non-actuated joint. Therefore, a novel method to solve this problem is proposed in this paper. The approach is validated by exploiting a simulation framework developed in MATLAB to solve the kinematics and dynamics of free-flying manipulators. The remainder of this paper is organized as follows: Section 2 outlines the background theory and the methodologies used; in Section 3, the solution of the inverse kinematics of the manipulator with the non-actuated joint is derived; Section 4 introduces the simulation setup, including the parameters and the validation methodology; Section 5 presents the results of simulations and discusses the performance of the proposed approach; and finally, Section 6 summarizes the contributions and outlines potential directions for future research.
# Locality-Sensitive Hashing for Information Retrieval System on Multiple GPGPU Devices
## 4 Searching Strategy for Multiple DLSH Clusters on Multiple GPGPU Devices  
Figure 6 represents our parallel searching system on multiple GPGPU devices, the data set is clustered using Algorithm 1 and sub-databases are stored by scattering in GPGPU memory. The GPGPU devices have been installed with the CUSTagedLSH search method detailed in Section 3. Our model consists of one Master process and several slave processes to control the GPGPU workflows. The Master process accept the queries and broadcasts them to the slaves. After multiple search stages, the Master combines and returns the \(k\)NN results of slaves. With this model, we have basically solved the problem of big database management. However, two new problems have arisen in this system, when using multiple devices:  
* Blind search (Problem 1): The search time for the same query may be different in different slaves. Without communication, the final search time is determined by the slowest slave. For example, slave 0 may need only 1 s to search (best-case scenario), while slave 1 takes 10 s (worst-case scenario); in this case, slave 0 must wait for slave 1 to finish before merging their results.
* Result overflow (Problem 2): The total number of \(k\)NN results among multiple slaves may exceed the rank size; that is, the slaves each determine their own \(k\)NN results, but the total \(k\)NN result may be higher than the rank size. This issue does not affect the accuracy, but the Master process requires more time to select the best \(k\)NN results and remove others.
# Network Attack Path Selection and Evaluation Based on Q-Learning
## 5 Simulation Evaluation
### Analysis of Experimental Results
#### 5.2.2 Experimental Results--Algorithm Comparison  
1. Compare the algorithm proposed in this paper with random attacks and selective attacks. Taking attack mode 1 as an example, the attack gains obtained after 100 attacks are shown in Figure 7a (its abscissa variables are the experiment times). It can be seen from the figure that compared with random attacks, selective attacks have a greater redirection through the best attack path to get the greatest attack gain, but the algorithm proposed in this paper shows obvious advantages. After learning 15 times, you can find the best attack path and get the maximum attack gain.
2. Compare the algorithm proposed in this article with the traditional Q-Learning algorithm. Use the traditional Q-Learning algorithm to perform path learning and attack gain calculation for attack mode 1 and attack mode 2. The comparison of the result with two methods is shown in Figure 7b. It can be seen from the figure that the attack gain obtained by the improved algorithm is consistent with the traditional algorithm, indicating that the algorithm in this paper has high accuracy. However, in terms of learning speed, the algorithm proposed in this paper can find the best attack path faster. Especially for the more complex attack mode (mode 1), the traditional algorithm needs 30 times of learning to get the best gain and reach convergence, while the algorithm in this paper only needs 15 times. The learning efficiency has nearly doubled, showing obvious advantages.
## 5 Discussion  
The problem of gathering data through the use of multiple mobile sinks, multiple rendezvous points, and various numbers of hops is a multiobjective problem. As a result, two types of metrics were used to compare the results. The first type was MOO metrics, where set coverage, hyper-volume, number of nondominated solutions, and convergence graphs were utilized. Networking metrics, including E2E delay, energy consumption, PDR, and trust, were also calculated.  
Regarding the first type, the results demonstrated that SC-MOPSO-DG is superior to NSGA-II in terms of most metrics and comparable to NSGA-III in terms of some metrics,  
Figure 14: Time series of PDR generated from our developed algorithm and the benchmarks.  
Figure 15: Time series of trust generated from our developed algorithm and the benchmarks.  
while being superior to NSGA-III in terms of hyper-volume. For example, it was competitive with NSGA-III in terms of domination and was superior in terms of hyper-volume.  
Regarding the second type, we only chose one solution from the Pareto front of each algorithm and generated the corresponding time series of the solution. The results were superior to those of the benchmarks in terms of PDR and E2E delay, demonstrating an excellent quality of service. Additionally, it showed a higher energy consumption profile and less trust, which is normal given the domination aspect of the solutions in the Pareto front and the self-conflicting nature between the objectives. The overall superiority of our developed SC-MOPSO-DG is attributable to the developed operators for class interaction, which enables moving solutions among various classes and consequently enabling variable-length searching.
# Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection
## 5 Experimental Results and Discussion
### Effect of the GA Population Size  
To evaluate the effect of GA population size on the performance of the algorithm, the Church dataset was applied. The GA population size was varied from 20 to 70 by steps of five individuals, and the performance of the algorithm was assessed under various outlier ratios. The results obtained from this experiment are illustrated in Figure 10.  
There was no considerable correlation between the accuracy and the size of the population. The average accuracy of inlier-detection (\(\alpha\)) with different population sizes was 99.1% with a standard deviation of only 0.9%. The accuracy of estimation (\(\mu_{d}\cdot CP\)) also remained below 0.05, which confirmed the stable accuracy of model estimation. The number of iterations before termination (_Itr_) seemed to be more dependent on the outlier percentage rather than the population size. The average number of iterations at outlier ratios less than 50% was 122, while it was only 62 at higher outlier ratios. The number of generated hypotheses (\(N_{model}\)) increased by either decreasing the outlier ratio or increasing the GA population size, since it depended both on the population size and the number of iterations. Showing that the accuracy does not have any distinct correlation with the population size, the moderate size of the population is suggested to both avoid the unnecessary generation of hypotheses and to keep the number of iterations small.  
Figure 9: Performance of the proposed algorithm with noisy images. The graph at the bottom of each surface-plot represents the average of respective performance criterion versus the amount of noise.
# AAGA: A Priori Adaptive Splitting Algorithm for the Split Delivery Vehicle Routing Problem
## 3 Performance Evaluation
### Benchmarking Instances  
We consider 4 benchmarking sets that include 88 instances overall as presented in Table 1. SET-1 [34] contains 21 instances, each with customers uniformly distributed on the perimeter of concentric circles centered at the depot. The instances are sorted by the number of customers and the number of concentric circles. SET-2 [36] and SET-3 [33] instances use similar customer demand profiles but vary in the number of customers and vehicle capacity. These demands are randomly sampled from the range \([aQ,bQ]\) with \((a,b)\) chosen by six different scenarios as in Table 1. SET-2 contains 14 and SET-3 contains 42 instances, 6 of which are repeated. Therefore, we test only the 36 of them that are not redundant. The coordinates of the customers in SET-2 are randomly generated using the coordinates of eil51, eil76, and eil101 from TSPLIB, which are also used in SET-4 [37]. SET-4 includes 11 instances where no specific rule is preserved throughout for generating the customer demands.
## 4 Verification  
The methodology for identifying subsynchronous oscillation parameters in power grids, leveraging the ERA as its foundation, underwent rigorous validation utilizing both artificially generated and authentic PMU data within the MATLAB R2021a environment. This comprehensive testing aimed to confirm not only the accuracy but also the practical feasibility and applicability of the proposed approach.  
The utilization of synthetic PMU measurements as the cornerstone for testing primarily stems from pragmatic concerns. While real-world data undeniably hold immense value in power system analysis, they inherently struggle to encompass the full spectrum of frequencies and oscillation traits. The intricate and dynamic nature of power grid states and faults poses a challenge, rendering it arduous to comprehensively capture all instances of SSO data. A controllable data set is required to measure the capability and trustworthiness of algorithms comprehensively. Therefore, synthetic data which can accurately set oscillation parameters is selected to simulate complex SSO scenarios. This process facilitates the assessment of algorithm performance across diverse scenarios, ultimately verifying their accuracy and suitability for real-world deployment and thereby bolstering overall system reliability.  
The adoption of genuine PMU data, being a vital component, is paramount, as it mirrors the intricate dynamic and oscillation behaviors of power systems in their operational states. Employing such data for algorithmic assessment enhances the precision in determining the algorithm's practical applicability and feasibility in real-life contexts. Concurrently, utilizing genuine PMU data facilitates a profound comprehension of the power system's dynamic traits, unveiling phenomena which may remain elusive in simulated environments and foster a superior grasp of operational protocols alongside potential hazards. However, it should be noted that actual measured PMU data may be affected by factors such as sensor errors and communication delays, which can have a certain impact on the accuracy and reliability of the data. In general, it is important to use actual PMU data measurements to validate the feasibility and practicality of algorithms in real-world scenarios.
## 3 Results and Discussion  
### Characterization of Crude Sword Bean Urease Extracts  
The main components of crude sword bean urease extracts, including total protein, amino acids, and soluble polysaccharides, are listed in Table 3. The presence of the urease  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Heavy Metal** & **Heavy Metal Concentration** & **Ca\({}^{2+}\) Concentration** & **Urea Concentration** & **Denoted as** \\  & **mg/L** & **mol/L** & **mol/L** & **Denoted as** \\ \hline Zn\({}^{2+}\) & 200 & 0/0.25/0.5 & 0.75 & GS-Zn0/0.25/0.5 \\ Ni\({}^{2+}\) & 200 & 0/0.25/0.5 & 0.75 & GS-Ni0/0.25/0.5 \\ Cr(VI) & 200 & 0/0.25/0.5 & 0.75 & GS-Cr0/0.25/0.5 \\ Zn\({}^{2+}\) + Ni\({}^{2+}\) + Cr(VI) & 200 + 200 & 0/0.25/0.5 & 0.75 & GS-multiple0/0.25/0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation conditions.  
enzyme in the crude sword bean urease extract was confirmed by the SDS-PAGE analysis. As shown in Figure 2, a band of relatively darker color compared to the baseline color (zero protein concentration) appeared at approximately 90 kDa in sword bean urease. This protein, with a molecular weight of 90 kDa, is reported to be \(\alpha\) subunits of urease [22]. Additionally, the SDS-PAGE analysis showed that one of the major proteins had a molecular weight of approximately 48 kDa, indicating that it is _Canavalin_, a component of the 7S seed globulin [33].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & \multicolumn{2}{c}{**Sword Bean Urease (g/L)**} \\ \hline  & Asp & 0.33 \\  & His & 0.084 \\  & Tyr & 0.16 \\  & Ile & 0.16 \\  & Val & 0.17 \\  & Ala & 0.35 \\  & Pro & 0.11 \\  & Arg & - \\ Amino acid & Lys & 0.24 \\  & Ser & 0.13 \\  & Glu & 0.12 \\  & Gly & 0.12 \\  & Met & 0.13 \\  & Leu & 0.13 \\  & Phe & 0.11 \\  & Thr & 0.11 \\ \hline Protein & & 4.5 \\ \hline \hline \multicolumn{2}{c}{Soluble polysaccharide} & \multicolumn{2}{c}{0.386} \\ \hline \hline \end{tabular}
\end{table}
Table 3: The concentrations of amino acids, protein, and soluble polysaccharide in SWU (“-” means below the detectable limit).  
Figure 2: SDS-PAGE analysis of the crude sword bean urease extract (SWU: sword bean urease).  
The crude urease extracts have the catalytic activity that facilitates the hydrolysis of urea. The EICP process is illustrated in Equations (1)-(4).  
\[\mathrm{CO(NH_{2})}_{2}+\mathrm{H_{2}O}\overset{\mathrm{Urease enzyme}}{\to}2 \mathrm{NH_{3}}+\mathrm{CO_{2}} \tag{1}\]  
\[2\mathrm{NH_{3}}+2\mathrm{H_{2}O}\leftrightarrow 2\mathrm{NH_{4}^{+}}+2\mathrm{OH^{-}} \tag{2}\]  
\[\mathrm{CO_{2}}+2\mathrm{OH^{-}}\leftrightarrow\mathrm{HCO_{3}^{-}}+\mathrm{OH^{ -}}\leftrightarrow\mathrm{CO_{3}^{2-}}+\mathrm{H_{2}O} \tag{3}\]  
\[\mathrm{Ca^{2+}}+\mathrm{CO_{3}^{2-}}\to\mathrm{CaCO_{3}} \tag{4}\]
## 2 Minimally Invasive Therapies for HCC and Their Tissular Effects
### Thermal Ablative Therapies
#### 2.1.5 Cryoablation  
Cryoablation (CA) uses the Joule-Thomson effect to cool the area of ablation by releasing high-pressure argon gas through a cryoprobe [62]. This leads to the formation of an iceball at temperatures below \(-150\)\({}^{\circ}\)C that is subsequently thawed [63]. Although cryoablation is not widely used, a randomized controlled trial has shown that 97% of HCC patients undergoing cryoablation had no tumor progression 1 year after the procedure and the overall survival rate at 5 years was 40% [64]. One of the advantages of cryoablation is that real-time observation of the ablated area may be performed through imaging methods, such as CT or US [65]. Technical success is considered when the iceball is at least 5 mm larger than the nodule in any direction [65]. However, small ablation volumes may be achieved with ease using CA in the case of small tumor nodules [66].  
CA is effective through two distinct mechanisms. On the one hand, it has direct effects on tumor tissue by forming ice crystals in the intra- and extracellular space as well as an increase in osmotic pressure, causing cell destruction and irreversible damage to cell organelles [24; 63]. Then, during thawing, the ice crystals converge into larger ones that further advance cellular injuries; moreover, due to osmotic pressure, water is displaced towards the intracellular space, expanding it and rupturing the cell membrane [63]. On the other hand, the indirect effects relate to the altering of blood flow towards the tumor due to damage to the endothelium of adjacent vessels. This leads to blood vessel thrombosis causing ischemia and cell death in the tributary volume of the vessel [24; 46]. A \"cold sink\" effect was described in CA, and therefore lesions near larger blood vessels may be more difficult to treat.  
Cryobiology has studied the effects of CA on living tissue and has shown that freezing triggers an anti-tumor response causing a T-cell response to the tumor [67; 68]. This effect, labeled \"cryoimmunology\", is a secondary advantage to using this ablative technique and the mechanisms involved are still being investigated.
## 3 Design of SIRAC
### The SI Algorithm
\right] \tag{28}\]  
Substituting Equation (15) into Equation (27) yields  
\[u_{d}(t)=B_{y}^{-1}\left[y_{d}^{(r)}(t)-A_{\xi}\xi_{d}(t)-A_{\eta}\eta_{d}(t)\right] \tag{29}\]  
where \(\left[A_{\xi},A_{\eta}\right]=A_{x}T^{-1}\). Let \(\xi(t)=\xi_{d}(t)\) and \(\eta(t)=\eta_{d}(t)\), and then Equation (25) can be rewritten as:  
\[\dot{\eta}_{d}(t)=\hat{A}_{3}\xi_{d}(t)+\hat{A}_{4}\eta_{d}(t)+\hat{B}_{2}B_{y }^{-1}\left[y_{d}^{(r)}(t)-A_{\xi}\xi_{d}(t)-A_{\eta}\eta_{d}(t)\right] \tag{30}\]  
Additionally, Equation (30) can be rewritten as:  
\[\dot{\eta}_{d}(t)=\hat{A}_{\eta}\eta_{d}(t)+\hat{B}_{\eta}Y_{d}(t) \tag{31}\]  
where \(Y_{d}(t)=\left[y_{d}^{(r)}(t),\xi_{d}(t)\right]^{T}\), \(\hat{A}_{\eta}=\hat{A}_{4}-\hat{B}_{2}B_{y}^{-1}A_{\eta}\), and \(\hat{B}_{\eta}=\left[ \begin{array}{c}\hat{B}_{2}B_{y}^{-1}\quad\hat{A}_{3}- \hat{B}_{2}B_{y}^{-1}A_{\xi}\end{array} \right]\). This is the inverse system. **Step 2:** Calculate \(\eta_{d}(t)\). A transformation \(M\) is used to decouple the system Equation (31) into a stable subsystem \(\sigma_{s}(t)\) and an unstable subsystem \(\sigma_{u}(t)\), that is,  
\[M\eta_{d}(t)=[\sigma_{s}(t),\sigma_{u}(t)]^{T} \tag{32}\]  
If \(A_{s}\) and \(A_{u}\) are eigenvalues of \(\hat{A}_{\eta}\), then \(diag(A_{s},A_{u})=M\hat{A}_{\eta}M^{-1}\). Additionally, Equation (31) can be rewritten as:  
\[ \begin{array}{l}\dot{\sigma}_{s}(t)=A_{s}\sigma_{s}(t)+B_{s}Y_{d}(t)\\ \dot{\sigma}_{u}(t)=A_{u}\sigma_{u}(t)+B_{u}Y_{d}(t)\end{array}  \tag{33}\]  
where \(\left[B_{s},B_{u}\right]^{T}=M\hat{B}_{\eta}\). The stable subsystem is integrated from \(t=0\) to \(t\rightarrow\infty\), while the unstable subsystem is integrated from \(t=0\) to \(t\rightarrow-\infty\). Subsequently, the bounded solution \(\eta_{d}(t)\) can be found. **Step 3:** Calculate \(u_{d}(t)\) and \(x_{d}(t)\). If a bounded solution of \(\eta_{d}(t)\) for the Equation (31) can be found, then the desired input \(u_{d}(t)\) can be obtained through Equation (29), and the associated desired state \(x_{d}(t)\) is obtained, as:  
\[x_{d}(t)=T^{-1}\left[ \begin{array}{c}\xi_{d}(t)\\ \eta_{d}(t)\end{array} \right] \tag{34}\]  
Figure 3 shows the whole process of the SI algorithm.