esa-sceva/satcom-chunk-collection huggingface dataset:
# Establishment of a Pressure Variation Model for the State Estimation of an Underwater Vehicle
## 2 Methodology and Results
### Pressure Variation Model (PVM)
#### 2.2.2 State Estimation
For the \(-20\) deg angle of attack with angular velocity 10 rad/s, the prediction showed a higher error rate of about 40% due to the relatively lower accuracy in the earlier velocity prediction (Table 18). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\alpha\) & & \(-\mathbf{10}\) & & \(-\mathbf{20}\) & & \(-\mathbf{30}\) & \\ \hline \(r\) & \(\mathbf{10}\) & \(\mathbf{20}\) & \(\mathbf{30}\) & \(\mathbf{10}\) & \(\mathbf{20}\) & \(\mathbf{30}\) & \(\mathbf{10}\) & \(\mathbf{20}\) & \(\mathbf{30}\) \\ \hline Predicted (knots) & 3.115 & 3.019 & 3.084 & 2.625 & 3.082 & 3.129 & 2.982 & 2.982 & 3.352 \\ Relative (Ext. (\%)) & 3.810 & 0.619 & 2.799 & 12.52 & 2.733 & 4.297 & 0.623 & 0.612 & 11.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Predicted velocities and relative errors for spiral motion (\(U=3.0\) knots, \(\beta=0\)). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(\alpha\) & & \(-\mathbf{10}\) & & \(-\mathbf{20}\) & & \(-\mathbf{30}\) & \\ \hline \(r\) & \(\mathbf{10}\) & \(\mathbf{20}\) & \(\mathbf{30}\) & \(\mathbf{10}\) & \(\mathbf{20}\) & \(\mathbf{30}\) & \(\mathbf{10}\) & \(\mathbf{20}\) & \(\mathbf{30}\) \\ \hline Predicted (knots) & 3.115 & 3.019 & 3.084 & 2.625 & 3.082 & 3.129 & 2.982 & 2.982 & 3.352 \\ Relative (Ext. (\%)) & 3.810 & 0.619 & 2.799 & 12.52 & 2.733 & 4.297 & 0.623 & 0.612 & 11.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Predicted drift angle and relative errors for turning with drift motion (\(U=3.0\) knots, \(\alpha=0\)). Ultimately, using the PVM constructed through the regression analysis based on CFD results, the estimated states of the AUV predict velocities and drift angles within the target range initially expected, with an error margin of 15% or less, similar to the prediction accuracy level of advanced research groups [10]. However, during the estimation of states in multiple motions, certain conditions display relatively high errors in the estimated drift angles, and it appears that there might be some discrepancies due to the absence of terms in the PVM that account for the correlation between drift angle and angular velocity. Therefore, further improvements in the PVM are needed to correct these issues.
## 1 Introduction
Such analysis is performed by varying the main MLT parameters, i.e., the number of transmitted beams and/or their angular separation. In the second part, a modified version of F-DMAS beamforming, called short-lag F-DMAS, is introduced; the simulated results of MLT imaging with short-lag F-DMAS beamforming are analyzed in the different MLT scenarios mentioned above, also studying how the maximum spatial lag between signals that enter this beamformer influences image quality. In the next sections, we will briefly recall the theoretical background of spatial coherence in ultrasound imaging and the main steps of the F-DMAS algorithm; the simulation setup will be described and the strategy used to study the trend of coherence with MLT will be presented (Section 2). Finally, the results of the coherence analytical study and F-DMAS performance in the considered test cases will be shown and discussed (Section 3). Conclusions will be provided in Section 4.
## 2 Study Sites and Database Description
### French Study Site
#### 2.1.1 Sentinel-1 Images  
The SAR images were obtained from the Sentinel-1A (S1A) and Sentinel-1B (S1B) satellite constellation operating at the C-band (wavelength \(\sim\)6 cm). Over our study site, S1A and S1B provide images every 6 days. The Sentinel-1 (S1) images are downloadable from the Copernicus website ([[https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)]([https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home))). The 25 S1 images used were acquired between September 2016 and May 2017 in IW imaging mode with the VV and VH polarizations (this acquisition mode is the primary conflict-free mode for land). In addition, S1 images were generated from the high-resolution Level-1 Ground Range Detected (GRD) product with a spatial resolution of 10 m \(\times\) 10 m. For all S1 images, the study site is imaged with an incidence angle (\(\theta\)) of approximately 39\({}^{\circ}\).  
The Sentinel-1 Toolbox (S1 TBX), developed by the ESA (European Spatial Agency), was used to calibrate the S1 images. The calibration aims to convert the digital number values of S1 images into backscattering coefficients (\(\sigma^{\circ}\)) in a linear unit. For each reference plot, the mean backscattering coefficient was calculated from each calibrated S1 image by averaging the \(\sigma^{\circ}\) values of all pixels within that plot. The average of \(\sigma^{\circ}\) pixels values reduces the speckle noise.
# Unified Analytic Melt-Shear Model in the Limit of Quantum Melting
## 4 Discussion
### Example: Beryllium  
Consider beryllium (\(Z=4\), \(A=9.0122\)) as an example of the application of quantum melting to a real material. In this case Equation (27) gives \(\rho^{*}\approx 1.3\cdot 10^{11}\) g/cc. The corresponding values of \(\rho_{G\max}\), \(\rho_{T_{\rm{H}}\max}\), \(G_{\max}\) and \(T_{m\max}\) are, respectively, \(1.1\cdot 10^{11}\) g/cm\({}^{3}\), \(7.8\cdot 10^{10}\) g/cm\({}^{3}\), \(5.0\cdot 10^{16}\) GPa and \(3.0\cdot 10^{7}\) K. The values of the five parameters used in the analytic form of \(G(\rho,0)\), Equation (13), come from the ambient data on Be found in the literature and are listed in Table 2.  
The cold shear modulus of Be in the unified analytic melt-shear model with quantum melting included is shown in Figure 2.  
Figure 3 details the previous figure as its lower-density portion. It compares the unified analytic melt-shear model with quantum melting included to the theoretical calculatons on the cold \(G\) of Be. Specifically, the values of cold \(G\) for both hcp-Be and bcc-Be were both calculated by ourselves using first principles based software VASP (Vienna Ab initio Simulations Package) and are taken from Refs. [19,20] as independent theoretical calculations on hcp-Be and bcc-Be, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(\rho_{0}\) (g/cm\({}^{3}\)) & \(G_{0}\) (GPa) & \(\gamma_{1}=\frac{7}{40}\,Z^{2/3}\) & \(\gamma_{2}\) & \(q_{2}\) \\ \hline
1.855 & 152.3 & 0.441 & \(-0.33\) & 0.4 \\ \hline \end{tabular}
\end{table}
Table 2: Five parameters of the unified analytic melt-shear thermoelasticity model of Be associated with its shear modulus.  
Figure 3: The lower-density portion of Figure 2: the cold \(G\) of Be in the unified analytic melt-shear model with quantum melting included (blue curve) vs. the theoretical calculations described in the text.  
Figure 2: The cold \(G\) of Be in the unified analytic melt-shear model with quantum melting included. \(G\) terminates at a critical density of \(1.3\cdot 10^{11}\) g/cm\({}^{3}\).
## 2 Improved Workflow for Fault Detection  
The workflow proposed in this paper consists of directional edge detection and edge enhancement. A flowchart of the improved workflow is shown in Figure 2, and the associated steps are summarized as follows:  
Figure 1: The conventional workflow for fault detection consists of conditioning, edge detection, and edge enhancement. It is set up to perform a significant number of sub-processes within each attribute.  
1. The amplitude contrast attribute was applied on the basis of the direction in which the edges are to be detected within the cube. This provides information about the major directions and edges of the fault to be investigated.
2. Summations were performed by selecting cubes with high fault connectivity on the basis of the previously acquired amplitude contrast attribute. This reduces noise and improves connectivity between edges.
3. The first edge enhancement was conducted using edge evidence. This attribute was applied sequentially to every direction to significantly reduce the noise and make the edges clearer.
4. The second edge enhancement was performed by ant tracking. A combination of passive and aggressive modes was used to enhance the edges of major or detailed faults.  
Upon comparing our method to the conventional workflow shown in Figure 1, it can be seen that the conditioning step is omitted in the studied method. This reduces the time required to process multiple processes, thereby rapidly providing the final results. Furthermore, a reduction in the processing step implies that the use of subjective input variable values is also reduced, which generates more objective results.
# Evaluating Pile-Supported Embankment Considering the Soil Anisotropy Effect
## 4 Soil Arching Considering the Cohesion Anisotropy  
### Cohesion Anisotropy  
[PERSON] and [PERSON] [51] (1974) conducted a study on the cohesion of soil slopes, considering the variability of soil characteristics based on the principal stress direction. They treated cohesion \(c\) as a variable that depends on the orientation of the principal stresses. To analyze the stability of soil slopes, they employed two types of cohesion parameters: vertical cohesion \(c_{v}\) and horizontal cohesion \(c_{h}\). These parameters accounted for the increase in cohesion linearly and unevenly with depth. This approach offers a more comprehensive understanding of the factors influencing slope stability in geotechnical engineering. The anisotropic cohesion \(c_{i}\), can be expressed as:  
\[c_{i}=c_{h}+(c_{v}-c_{h})\cos^{2}i \tag{19}\]  
where \(c_{v}\), \(c_{h}\), represent the cohesion in the vertical direction and horizontal direction, respectively; \(i\) represents the angle between the large principal stress and the vertical direction, which is determined by the following formula in the two-dimensional case:  
\[\cos 2i=\frac{\sigma_{z}-\sigma_{x}}{2R} \tag{20}\]  
\[R=\frac{1}{2}\sqrt{\left(\sigma_{x}-\sigma_{z}\right)^{2}+4\tau_{xz}^{2}} \tag{21}\]  
where \(\sigma_{x}\), \(\sigma_{z}\), and \(\tau_{xz}\) represent horizontal stress, vertical stress, and shear stress, respectively.  
In the context of three-dimensional geotechnical analysis, the soil is considered to exhibit transverse isotropy [52]. The principal stress direction angles within the \(xz\) plane are ascertained by \(\sigma_{x}\), \(\sigma_{z}\), and \(\tau_{xz}\), while those in the \(yz\) plane are ascertained by \(\sigma_{y}\), \(\sigma_{z}\), and \(\tau_{yz}\). These calculations yield the corresponding directional angles for both planes. Consequently, the larger angle values from them were adopted to compute the cohesive forces at the location of the integration point.  
If the anisotropy coefficient \(k\) is defined as \(k=c_{h}/c_{v}\), Equation (19) can be expressed as:  
\[c_{i}=c_{v}\Big{(}k+(1-k)\cos^{2}i\Big{)} \tag{22}\]  
To consider the anisotropy of cohesion, the Mohr-Coulomb yield function replaces the constant \(c\) with \(c_{i}\), resulting in the Mohr-Coulomb yield criterion (Equation (23)).  
\[F=R_{mc}q-p\tan\varphi-c_{i}=0 \tag{23}\]
# Adaptive Multi-Grained Buffer Management for Database Systems
## 2 Related Work  
### Traditional Buffer Management  
Buffer management is a fundamental component in traditional disk-based database systems [1; 2; 3]. The database buffer is organized as a buffer tool, an area of main memory used by DBMSs to maintain recently used data. The buffer pool is divided into many fixed-sized frames. In general, each frame has the same size as a disk page. When a user query requests a page, the DBMS returns the page address in the buffer pool if the requested page is in the buffer pool; otherwise, the buffer manager calls the storage manager to load the requested page from the disk. Due to the limited memory space, when the buffer pool is full, the buffer manager must perform a page replacement policy to select a page, namely, victim page, which will be evicted out of the buffer [9; 10]. The design of replacement strategies has a significant impact on the performance of the buffer manager [11; 12].  
In the past two decades, many well-known buffering policies have been proposed, e.g., LRU and LIRS [2]. Most of these algorithms have been well explained in textbooks; thus, in this paper, we will not give many details about the existing buffering schemes. The literature [1] presents a good survey on traditional buffer management policies. Although in recent years, SSDs have accomplished notable improvements over the years, HDDs are still widely used as enterprise storage [13; 14].  
Recently, we proposed MG-Buffer [7] that adds a new tuple buffer for the traditional page buffer, which inspires this study. MG-Buffer always moves the hot tuples in the page buffer to the tuple buffer when a page is evicted out of the page buffer. However, MG-Buffer has two limitations. First, MG-Buffer always manages hot data in a tuple granularity, which loses the benefit of spatial locality, because the hot tuples are from different pages. Second, writing dirty pages may incur additional read I/Os. If a dirty page evicted from the page buffer only has few tuples modified, MG-Buffer will always move these tuples to the tuple buffer. Then, when a dirty tuple needs to be written to disks, it has to reload the original page and synchronize the dirty tuple before flushing. Our design improves MG-Buffer by proposing a new adaptive scheme to make MG-Buffer suitable for various access patterns, and our experiments will demonstrate the superiority of our design over MG-Buffer.
# Dynamic Response Analysis of Mega-Sub Isolated Structures under Multiaxial Earthquakes
## 3 Analysis of the Influence of Horizontal Parameters of the Mega-Sub Isolated Structure
### Analysis of the Influence of the Horizontal Mass Ratio of the Seismic Isolation Layer
#### 3.2.1 Mass Ratio Influence on Main Frame Response  
In the following, the change of the mass ratio of the primary and secondary frame is realized by changing the mass of the isolation subframe, and the research and analysis are carried out. The mass ratio \(\omega\) is defined as (\(\omega\) = total mass of the isolation sub-frame/total mass of the mega-frame), and the value of the mass ratio \(\omega\) is the range is (0.1-4.5), and the damping ratio of the isolation layer is 0.3.  
Inputting an X-direction El Centro seismic wave whose seismic acceleration peak value is 0.15 g, the displacement and acceleration response peak values of the top layer of the main frame vary with the mass ratio as shown in Figure 3 below.  
From the dynamic response curve of the main frame, it can be seen that the peak displacement and acceleration tend to decrease overall with increasing mass ratio \(\omega\), with the following pattern.  
1. For the displacement response of the main frame, as the mass ratio \(\omega\) is less than 1.0, the displacement of the top floor of the main frame decreases faster as the mass ratio increases, i.e., the mass of the isolated substructure increases; when the mass ratio continues to increase by more than 1.5, the peak displacement response trend continues to decrease until it flattens out.
2. For the acceleration response, when the mass ratio \(\omega\) is between (0.1 and 1.0), the acceleration decreases relatively quickly, and the acceleration response is more sensitive to the change in mass ratio; when the mass ratio continues to increase, the acceleration decreases very slowly and tends to level off, and the damping effect is better at this time.  
From the data, when the mass ratio \(\omega\) is greater than 2.5, the main frame displacement and acceleration response peaks are smaller. The vibration-damping effect is relatively better, and increasing the mass ratio has little influence on the structural response. Considering the actual situation of the project, it is recommended to set the mass ratio between 2.5 and 4.0; at this time, the mass of the substructure reaches 71.4% to 80% of the total weight of the building.
## 4 Cascade Experiment  
### Basic Principle of Ring Network Test  
There are various ways to connect the links of an optical fiber time transfer system. Dense wavelength division multiplexing (DWDM) technology is widely used in most of the existing schemes. The advantage of this technology is that it can reduce the total number of required optical fibers and save optical fiber resources under a given information transmission capacity. For the long-distance optical fiber time synchronization system, the cascaded mode can also be used. The advantage of cascading is that the noise of the optical fiber link is not accumulated. The cascade receiver of the remote site can take out the time synchronization signal, which can be used directly by the next site of optical fiber time transfer local terminal equipment. The disadvantage is that the error introduced by the compensation mechanism in the cascade process will accumulate. Three nodes were chosen for this article. On the 150 km optical fiber link composed of three 50 km optical fibers, the secure optical fiber ring time service network was completed by using three encrypted secure time transfer devices.  
According to the same encryption mode as a single device, a security encryption cascade test was carried out on the ring network. The optical fiber time transfer system encrypted the information of TIC at the first local site via the encryptor. After decryption at the first remote site, the receiver received the decrypted data from the external link and compared them with the data transmitted via the optical fiber. If the data were the same, the optical fiber time synchronization of the first cascade system was completed by using Formula (3). Finally, the time synchronization of the whole optical fiber cascaded system was completed by using three equipment units in the same way, as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c} \hline
**Data before Encryption** & 249,746,186 \\ \hline \multirow{4}{*}{**Public key Pn**} & x = 435B39 CC\_ASF3B508\_C1488 AFC\_67 BE491A\_ \\  & 0 FBA07E\_581A0E48\_49A5 CF70\_628A7E0A \\  & y = 75 DDBA78\_F15 FEECB\_4C7895E\_C1 CDF5 FE\_ \\  & 01 DEBB2C\_DBADF453\_90 CCFF77B\_BA076A42 \\ \hline \multirow{2}{*}{**Random k**} & 4C62 EEFD\_6 ECFC2B9\_5B92 FDc6\_3D957514\_ \\  & 8 AFA1742\_5546D490\_18E5388D\_49 DD7B4F \\ \hline \multirow{4}{*}{**Ciphertext C\_out**} & 04245c26 fb68b1 dddddb12c4b6b9f92b6d5 fe60a838b0d18d1c144 abf17f6252e \\  & 776 cb9264c2a7e88e52b19903 fdc47378f60e536811f5c07423a24b84400f01b8 \\  & 325a09 edd6078149e61d7 bc237a15 eb7b18c546e2a2 ef00e814688574958 bce8b870 ceb43988c4546a \\ \hline \multirow{2}{*}{**Private key dn**} & 1649 AB7\_A00637 BD\_52 EEFE28\_3 FB3535\_ \\  & 34 AA/F7C\_B89463F2\_08 DDBC29\_20 BB0 DA0 \\ \hline
**After decryption ASCII** & 32 34 39 37 34 36 31 38 39 32 34 39 37 34 36 31 38 39 \\ \hline
**Decrypted plaintext** & 249,746,186 \\ \hline \end{tabular}
\end{table}
Table 2: Encryption and decryption data table via 50 km optical fiber link.
# Deep Learning Optimisation of Static Malware Detection with Grid Search and Covering Arrays
## 5 Grid Search versus cAgen for NN Hyperparameter-Optimisation Tasks
### cAgen  
To use cAgen for hyperparameter optimisation, certain parameters need to be defined beforehand. Firstly, a workspace needs to be created to prepare the model. Next, the input parameter (IPM) is selected to modify the model's parameters. The final step is to set the value of t, which determines the strength of the covering array. Due to the large size of the Ember dataset, we only investigated covering arrays of strengths 1 and 2. However, for the Kaggle dataset, we explored covering arrays of strengths 1, 2, and 3.  
Structurally, cAgen involves creating a covering array, a matrix representing a set of parameter configurations. Each row in the matrix represents a unique configuration of hyperparameters, and the columns represent the individual hyperparameters. The steps involved in the cAgen process are:  
1. Create a workspace to prepare the model;
2. Select the input parameter (IPM) to modify the model's parameters;
3. Set the value of t, which determines the strength of the covering array;
4. Generate a covering array of hyperparameter configurations using the IPM and t-value; For each configuration in the covering array:
5. Train a model on the training set using the current hyperparameters;
6. Evaluate the model's performance on the validation set;
7. Select the hyperparameters that result in the best performance on the validation set.  
Behaviourally, cAgen can be described as an optimisation method that uses a covering array to explore a subset of the search space rather than exhaustively searching through all possible combinations of hyperparameters. This approach can be more efficient than grid search for large search spaces, as it reduces the number of configurations needing exploration. However, the quality of the covering array depends on the strength value and the choice of input parameters, which can affect the effectiveness of the optimisation process. The implementation details for using cAgen with neural networks are shown below in Figures 3 and 4.  
Figure 3: Flowchart for cAgen implementation details.
# Deployment Optimization of Defense Stations in an Attack-Defense Aerial War Game
## 2 Game Design
### Deployment of Defense Stations
In [23], an adaptive simulated annealing-particle swarm optimization (SA-PSO) algorithm was proposed to optimize the relative distances and angles of a leader and its follower missiles in a cooperative multi-missile formation to attain combat effectiveness. The formation was optimized to accomplish different missions. An evaluation index system was designed for the engagement of missile formations, based on an analytic hierarchy process (AHP), and was used to calculate the fitness function. The proposed algorithm converges faster than the basic and adaptive PSO algorithms, and the optimal formation was acquired quickly and effectively. In [24], the major factors affecting the defense deployment of air defense weapons against intelligent air strike weapons were analyzed. An adaptive PSO algorithm was proposed to compute the minimum kill path to air strike weapons, and was embedded in the fitness function of an outer PSO algorithm to form an adaptive nested PSO algorithm. The latter was used to obtain the optimal defense deployment of air defense weapons. In this work, we use the basic PSO algorithm because its convergence performance is fair enough for our purpose. Due to imperfect kill probability, there is no steady-state optimal solution to converge to, even by applying other improved versions of PSO or machine learning algorithms. Considering the trade-off among computational load, convergence, and complexity, we used basic PSO in this work.