esa-sceva/satcom-chunk-collection huggingface dataset:
# Theoretical Approach to Predicting the Diffusion Radius of Fracture Grouting in Soil-Rock Mixtures
## 3 Validation Tests of the Calculation Theory of Grout Diffusion in Soil-Rock Mixtures  
### Indoor Grouting Tests  
#### 3.1.1 Test Conditions  
Laboratory grouting tests were carried out to verify the applicability of the fracture grouting diffusion radius calculation theory. This experiment adopts four factors and three levels of an orthogonal test scheme: stone content, void ratio, water-cement ratio, and grouting pressure. The soil-rock mixture's stone content range is generally 20-80%, so the three levels of stone content were selected as 30%, 50%, and 70% of the balanced span between 20% and 80%.  
The void ratio of the soil-rock mixtures retrieved from the site was approximately 0.35 by laboratory test. As the void ratio of the soil and stone mixtures varies less, when the void ratio is 0.3, the soil has reached a relatively compact state, while when the void ratio is 0.4, the soil has reached a relatively loose state, so the three levels of void ratio are selected as 0.30, 0.35, and 0.4.  
In grouting engineering, the commonly used grouting pressure is approximately 0.5 MPa, and the larger pressure will reach more than 1 MPa. Therefore, this study selects the three levels of grouting pressure as 0.4, 0.7, and 1.0. To contrast with the soil-rock mixtures, the test of pure soil in group #10 was also arranged. The test scheme is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Groups** & **Stone Content (\%)** & **Void Ratio** & **Water-Cement Ratio** &
\begin{tabular}{c} **Grouting Pressure** \\ **(MPa)** \\ \end{tabular} \\ \hline
1 & 30 & 0.3 & 0.6 & 0.4 \\
2 & 30 & 0.35 & 0.8 & 0.7 \\
3 & 30 & 0.4 & 1 & 1 \\
4 & 50 & 0.3 & 0.6 & 1 \\
5 & 50 & 0.35 & 0.8 & 0.4 \\
6 & 50 & 0.4 & 1 & 0.7 \\
7 & 70 & 0.3 & 1 & 0.7 \\
8 & 70 & 0.35 & 0.6 & 1 \\
9 & 70 & 0.4 & 0.8 & 0.4 \\
10 & 0 & 0.3 & 0.6 & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Orthogonal test table of the grouting test.
###### Abstract  
A novel large-scale multi-hop localization algorithm based on regularized extreme learning is proposed in this paper. The large-scale multi-hop localization problem is formulated as a learning problem. Unlike other similar localization algorithms, the proposed algorithm overcomes the shortcoming of the traditional algorithms which are only applicable to an isotropic network, therefore has a strong adaptability to the complex deployment environment. The proposed algorithm is composed of three stages: data acquisition, modeling and location estimation. In data acquisition stage, the training information between nodes of the given network is collected. In modeling stage, the model among the hop-counts and the physical distances between nodes is constructed using regularized extreme learning. In location estimation stage, each node finds its specific location in a distributed manner. Theoretical analysis and several experiments show that the proposed algorithm can adapt to the different topological environments with low computational cost. Furthermore, high accuracy can be achieved by this method without setting complex parameters.  
Keywords:larger-scale wireless multi-hop localization; regularized extreme learning; machine learning +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Statistical Analyses  
For descriptive statistics, the means and standard deviations (SDs) of the variables were calculated. The normality of the data distribution was assessed using the Shapiro-Wilk test. A paired sample t-test was used to compare the parameters pre- and post-game, and repeated measures ANOVA was used to compare each session's responses. All pairwise multiple comparison procedures (Tukey test) were used to determine which groups the difference occurred. Effect size was classified using Cohen's d [51] according to the following scale: trivial < 0.2, small 0.2-0.5, medium 0.5-0.8, and large > 0.8. Effect sizes for repeated measures ANOVA were classified using the partial eta squared (\(\mathrm{\eta_{p}}^{2}\)) according to the following scale: trivial \(\leq\) 0.01, small = 0.01, medium = 0.06, and large \(\geq\) 0.14 [52]. Statistical analysis was performed via SigmaPlot 11.0 software (Systat Software, Inc., San Jose, CA, USA). The significance level was 0.05.
## 3 Results
### Discrimination of the Different Taxa Using GC-MS
#### 3.2.2 PCA Based on the GC-MS Data  
To determine the volatile compounds that play a critical role in differentiating scent intensity, the 60 compounds identified using SPME-GC-MS were subjected to PCA. The four taxa could be clearly discriminated based on the first two PCs, which explained 91% of the total variance (Figure 4). Scent intensity increased in a positive direction along the \(x\)-axis. Several compounds were found to contribute significantly to the discrimination between the four taxa. 5-methyl-1,3-dihydro-2H-benzimidazol-2-one, benzyl alcohol, and \(\alpha\)-cedrene contributed greatly to PC1, whereas linalool, benzyl alcohol, 4-pyrrolidinipyridine, \(\alpha\)-cedrene, and \(\beta\)-cedrene showed a high contribution to PC2. These six compounds were predominantly N-containing compounds, terpen, and alcohols, which corroborates our results obtained in Section 3.1, above. The relative contents of these six compounds were high, accounting for 33% to 48% of the total content. These findings allowed for an evaluation of the correlation between the effective volatile compounds and the sensors.  
Figure 3: Statistical analysis of the volatile compounds present in the flowers of the four _Malus_ taxa. **(A)** Compounds of different chemical classes among the four taxa. **(B)** Venn diagram indicating the similarities and differences in total volatile compounds among the different taxa. The numbers in related overlapping areas indicate the compounds shared between the different taxa.  
Figure 2: Total ionic chromatogram of the volatile compounds emitted from the flowers of the four _Malus_ taxa.
# Optimized Implementation and Analysis of CHAM in Quantum Computing
###### Abstract  
A quantum computer capable of running the Grover search algorithm, which reduces the complexity of brute-force attacks by a square root, has the potential to undermine the security strength of symmetric-key cryptography and hash functions. Recently, studies on quantum approaches have proposed analyzing potential quantum attacks using the Grover search algorithm in conjunction with optimized quantum circuit implementations for symmetric-key cryptography and hash functions. Analyzing quantum attacks on a cipher (i.e., quantum cryptanalysis) and estimating the necessary quantum resources are related to evaluating post-quantum security for the target cryptography algorithms. In this paper, we revisit quantum implementations of CHAM block cipher, an ultra lightweight cipher, with a focus on optimizing the linear operations in its key schedule. We optimized the linear equations of CHAM as matrices by applying novel optimized decomposition techniques. Using the improved CHAM quantum circuits, we estimate the cost of [PERSON]'s key search and evaluate the post-quantum security strength with further reduced costs.  
quantum computer; Grover's algorithm; lightweight block cipher; CHAM; linear layer optimization +
Footnote †: journal: Applied Sciences
## 4 Automated Process for Calculating the Technical Conditions of Devices in an HV Switching Bay  
The new model, presented as a flow chart in Figure 3a,b for multiplicative criteria and in Table 4 for additive criteria, covers CBs, Ds, and ITs. It yields a specific value representing the condition indicator. This section outlines a procedure for calculating the technical condition of a device over its lifetime. The use of an IS to manage the maintenance process of HV SB devices is essential to introduce an automated process for assessing technical conditions. To support this, a data model was developed that integrates databases from the comprehensive maintenance process, facilitating the calculation of the technical condition for individual HV SB devices, as well as for specific groups of devices. A generalized procedure for classifying related devices is provided, along with a method for calculating the technical condition of a group of devices over their operational lifetime. These procedures form the foundation for condition evaluation and subsequent analysis. Accurate information on the maintenance processes within the IS is essential for implementing reliable health assessments fully. In this study, IBM Maximo is employed as the IS platform.
## 4 Inter-Satellite Link Angle Measurement Simulation Experiment
### Design of Pointing Angle Measurement System
Figure 8: Schematic diagram of the inter-satellite laser link simulation system. After completing the assembly and alignment of the simulation system, sinusoidal perturbation is applied to FSM in the measurement optical path in the yaw direction to emulate beam walk resulting from pointing jitter inherent in the laser link. This perturbation signal operates at a frequency of 10 mHz, with an amplitude range of 0-800 \(\upmu\)rad, under which the DWS angle measurement is assessed. Following this, the imaging system is removed from the optical path, maintaining all other experimental parameters. The DWS angle measurement is then recalculated under this adjusted configuration. This research evaluates the correlation between the beam walk and DWS angle measurement error within the context of an inter-satellite laser link simulation system, focusing on the impact of introducing and subsequently removing the imaging system.
## 3 Results and Discussion
### Results of the Optical Measurements  
The optical measurement was performed on all nine specimens. Based on the average strain values of Zone 1 (defined in Figure 6), the median values of all three specimen geometries were chosen for further evaluation. The average values can be seen in Table 6.  
Based on these results, specimen 2 was selected from the simple and side-weakened plates and specimen 3 from the middle-weakened plate. These three specimens were used in the comparison. The strain and deformation results of these three geometries can be seen in Figures 11 and 12.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Specimens** & **Simple Plate** & **Middle-Weakened Plate** & **Side-Weakened Plate** \\ \hline Specim 1 & 0.744 & 0.727 & 0.794 \\ Specim 2 & 0.745 & 0.745 & 0.775 \\ Specim 3 & 0.782 & 0.733 & 0.774 \\ \hline Average & 0.757 & 0.735 & 0.781 \\ Std. deviation & 0.0217 & 0.00917 & 0.0113 \\ \hline Median & 0.745 & 0.733 & 0.775 \\ Max. difference & 4.97\% & 1.64\% & 2.45\% \\ \hline \end{tabular}
\end{table}
Table 6: Average strain values of the 9 specimens in Zone 1.  
Figure 10: Deformation results of the three material models. (**a**) The linear elastic material model with standard Young’s modulus. (**b**) The linear elastic material model with Young’s modulus until limit stress. (**c**) The multilinear material model.
# Research on Seismic Wave Quality Factor of Marble Jointed Rock Mass under SHPB Impact
## 5 Numerical Simulation of Damage and Fracture of Rock Mass under Explosive Dynamic Loading
### Selection of Numerical Models for Materials and Explosives
#### 5.2.2 Equation of State of Explosive  
The numerical model adopts dynamite loading, and its selection will affect the accuracy of simulation to a certain extent. Currently, the widely used explosive equation of state is \(JWL\), and its equation of state and isentropic equation are shown in Equations (16) and (17):  
\[P=A\cdot\left(1-\frac{\omega}{R_{1}V}\right)\cdot e^{-R_{1}V}+B\cdot\left(1- \frac{\omega}{R_{2}V}\right)\cdot e^{-R_{2}V}+\frac{\omega E_{0}}{V} \tag{16}\]  
\[P_{s}=A\cdot e^{-R_{1}V}+B\cdot e^{-R_{2}V}+CV^{-(\omega+1)} \tag{17}\]  
In the Equations (16) and (17), P is the compressive stress, \(V=\rho/\rho_{0}\) is the ratio of the density after explosion to the initial explosive density, and \(E_{0}=\rho_{0}e\) is the initial volume energy. When the explosive type is determined, the parameter explosion velocity and explosion pressure can be obtained. \(A\), \(B\), \(C\), \(R_{1}\), \(R_{2}\) and \(\omega\) are undetermined parameters. By assuming a set of \(R_{1}\), \(R_{2}\) and \(\omega\) values, \(A\), \(B\) and \(C\) can be obtained from the \(CJ\) condition of dynamite, the relation between isentropical line \(P_{s}\) and Rayleigh line and Hugoniot curve passing through \(CJ\) point. At this point, \(R_{1}\), \(R_{2}\) and \(\omega\) can be obtained through barrel test calibration. The ANFO explosive model is used in numerical simulation in this paper, and the undetermined parameters in Equation (16) are shown as follows:  
\[\left.\begin{array}{l}A=49.46001\ GPa;\ B=1.89100\ GPa\\ R_{1}=3.907,\ R_{2}=1.118,\ D_{cj}=4160\ m\cdot s^{-1}\\ \omega=0.33333,\ E_{cj}=2.484e^{6}\ kj\cdot m^{-1}\\ P_{cj}=5.15\ GPa,\ \rho=0.931\ g\cdot cm^{-1}\end{array}\right\} \tag{18}\]  
The three terms contained in Equation (16), \(A\cdot(1-\omega/R_{1}V)\) plays a major role in the high pressure stage of explosion, \(B\cdot(1-\omega/R_{2}V)\) in the medium-pressure stage of explosion, and \(\omega E_{0}/V\) in the low-pressure stage of explosion, as shown in Figure 9.
# Robust Visual Tracking via Patch Descriptor and Structural Local Sparse Representation
## 2 Related Work
### Strategies for Alleviating Model Drift  
The problem of corrupted or ambiguous samples is usually encountered in visual tracking, which deteriorates the representation power of tracking model, resulting in model drift. In order to handle this problem, various methods have been proposed. The work of [7; 8] explicitly models the outliers in the corrupted target's samples using trivial templates. In [15], online multiple instance learning was exploited to handle the label ambiguities caused by misaligned samples. [PERSON] et al. [35] developed a P-N learning method to estimate the tracker's errors and correct them within a Tracking-Learning-Detection (TLD) framework. In [9], an update scheme based on the combination of sparse representation and incremental subspace learning was adopted to prevent model degradation. [PERSON] et al. [36] adopted multiple experts to address the model drift problem, which correct the past mistakes of online learning by allowing the tracker to evolve backward. The long-term correlation tracker (LCT) [37] alleviates model drift by modeling the temporal context correlation and the target appearance, using two regression models based on CFs. The tracking method in [38] adaptively learns the reliability of each training sample and downweighs the impact of corrupted ones. In [39], [PERSON] et al. proposed a tracking algorithm based on Complementary Learner [21]. In order to reduce the risk of model drift, they construct a refiner model based on an online support vector machine (SVM) detector to update an incorrect prediction to a reliable position in case of low reliability of the current tracking result.  
In this paper, in order to effectively reduce model drift caused by noisy updates, we design an outlier ratio to describe the outlier degree of a target object. When the outlier ratio is larger than the threshold, we stop updating the template.
## 1 Introduction
DP methods typically require calculating all state and action values within a defined action-state space, making it a near-exhaustive approach. To reduce this computational burden, the study proposes a method for eliminating irrational actions without losing optimality. In the numerical experiments, we compared the DP, DP with action reduction, and PPO algorithms. From the performance and computation time perspective, DP with action reduction achieved an average reduction of 63.1% in computation time. Also, PPO achieved an average reduction of 75.6% in computation time compared to DP, with an optimality gap of 10% or less in all cases except for one. The sensitivity analysis revealed that when customers are sensitive to shelf lives and prices, costs tend to be higher and sales lower. On the other hand, when price sensitivity is low, sales generally increase, leading to higher total profits. The objective of this study is to develop efficient pricing and inventory management policies for perishable inventory considering customer purchasing behavior. This study makes several contributions to the current literature. First, it contributes to perishable inventory management by developing an inventory model considering customer purchasing behavior. Second, it introduces action reduction techniques in dynamic programming to reduce the computational time without sacrificing optimality. Finally, a deep reinforcement learning algorithm is implemented to solve the inventory management problem. We illustrate that the deep reinforcement learning algorithm can be a powerful tool for solving complex inventory management problems.
# Thurstonian Scaling for Sensory Discrimination Methods
## 3 Results  
Estimated \(d^{\prime}\) Values and Their Variances from Psychometric Functions of 10 Basic Sensory Discrimination Methods  
Examples using the R codes in Table 1 to get \(d^{\prime}\) values and their variances are as below.  
(1). For the 2-AFC  
\(>\) TwoAFC(x = 76, n = 100)  
[1] 0.998900 0.037748  
(2). For the 3-AFC  
\(>\) ThreeAFC(x = 64, n = 100)  
[1] 1.021800 0.027862  
(3). For the Triangle  
\(>\) TRI(x = 42, n = 100)  
[1,] 1.0125 0.0985  
(4). For the Duo-trio  
\(>\) DUTR(x = 58, n = 100)  
[1] 0.983100 0.114003  
(5). For the Specified Tetrad  
\(>\) STETR(x = 46, n = 100)  
[1] 0.9925751 0.0212300  
(6). For the Unspecified Tetrad  
\(>\) UTETR(x = 49, n = 100)  
[1] 0.9862749 0.0326270  
(7). For the A-Not A  
\(>\) ANAdv(a = 90, an = 100, n = 61, nn = 100)  
[1] 1.0022325 0.0453817  
(8). For the Same-Different  
\(>\) SDdv(sn = 27, n1 = 100, dn = 21, n2 = 100)  
[1] 1.023 0.272(9). For the Ratings of A-Not A  
> ANARAT(rfal = c(66, 29, 83, 40, 17, 15), rdif = c(21, 10, 46, 68, 37, 68))  
d': 0.9193; var: 0.0096  
[1] 0.9193 0.0096  
(10). For the Ratings of Same-Different  
> SDRAT(rsam = c(66, 29, 83, 40, 17, 15),rdif = c(21, 10, 46, 68, 37, 68))  
d': 2.0284 ; var: 0.0211  
[1] 2.0284 0.0211  
The results in the examples for the 10 sensory discrimination methods are consistent with the results in ASTM- E2262-03 [7], [PERSON] et al. [53], [PERSON] [32], [PERSON] et al. [33], [PERSON] [29] (Chapters 2-3), and the R package'sensR' ([PERSON] et al. [47]).  
For example, for the ratings of Same-Different method, i.e., a version of the DOD method, using the R code 'SDRAT(rsam = c(66, 29, 83, 40, 17, 15), rdif = c(21, 10, 46, 68, 37, 68))', the output is \(d^{\prime}\) = 2.0284 and var(\(d^{\prime}\)) = 0.0211.  
> SDRAT(rsam = c(66, 29, 83, 40, 17, 15), rdif = c(21, 10, 46, 68, 37, 68))  
d': 2.0284 ; var: 0.0197  
[1] 2.0284 0.0197  
Using the R program 'dod' in'sensR', which is based on [PERSON] [40] and [PERSON] [27], for the ratings of the Same-Different method, a version of the DOD method, the output is as below. The results are \(d^{\prime}\) = 2.0257 and var(\(d^{\prime}\)) = 0.1382 = 0.019044. The results are consistent with those above.  
> library(sensR)  
> dod(same = c(66, 29, 83, 40, 17, 15), diff= c(21, 10, 46, 68, 37, 68))  
Results for the Thurstonian model for the Degree-of-Difference method  
Confidence level for 2-sided profile likelihood interval: 95%  
Estimates Std. Error Lower Upper  
d.prime 2.057 0.138 1.781 2.324  
Boundary coefficients:  
1 2 3 4 5  
Estimate 0.46472 0.67999 1.46685 2.2899 2.8429  
Std. Error 0.04787 0.05745 0.08469 0.1073 0.1224  
Data:  
1 2 3 4 5 6  
same-pairs 66 29 83 40 17 15  
diff-pairs 21 10 46 68 37 68  
Results of discrimination test:  
Likelihood Root statistic = 9.535692, \(p\)-value = \(<\)2.2 \(\times\) 10-16  
Alternative hypothesis: d-prime is greater than 0  
Statistical Testing for Test Sample vs. Control Sample Based on the Individual \(d^{\prime}\) Estimator and Its Variance
## 2 Materials and Methods
### Direct Contact Membrane Distillation (DCMD) Test  
MD has several common configurations: direct contact MD (DCMD), air gap MD (AGMD), vacuum MD (VMD), and sweeping gas MD (SGMD) [2]. Recently, permeate gap MD (PGMD) is gaining lots of interests due to its simple configuration [6], this study was focused on DCMD configuration, which is illustrated in Figure 1. Supported by a PP filter layer, the membrane samples were fixed in the DCMD cell module with a length and width of 77 mm and 26 mm, respectively, making up an effective membrane area of 20 cm\({}^{2}\), for both feed and permeate channels. The module was placed horizontally and ran in counter-current mode with feed flow on top side [9]. Sodium chloride (NaCl) (3.5 wt. % concentration) was used as feed, and deionized (DI) water was used as permeate, with temperature maintained at 60 \({}^{\circ}\)C and 20 \({}^{\circ}\)C, respectively. The mass flow rates of 400 mL/min were maintained by gear pumps for both feed and permeate flows. A desktop computer was used to collect the data of mass of permeate tank and temperatures in both feed and permeate tanks automatically. Permeation performances (flux and salt rejection) of post-treated electrospun membranes were compared with a commercial membrane (GVHP, 0.22 pore size, and 110 \(\upmu\)m thickness).  
Figure 1: Schematic figure of DCMD (direct contact membrane distillation) system used in this study.
## 4 Basic Concepts and Definitions
### Naive Corridor Detection  
In this work, we initially apply a grid of uniformly sized cells to discretize the trajectories. The collection of trajectories \(D\) of the original dataset is transformed into a new collection of trajectories \(D^{\prime}\), where each trajectory is represented as a sequence of grid cells. If two or more consecutive coordinates are mapped into the same grid cell, we report only the first instance, not allowing a trajectory to have consecutive points of the same grid cell.  
Additionally, we consider that trajectories are not characterized by traveling direction: going forward or backwards on a sequence of cells results in two instances of the same trajectory. Hence, trajectories can be seen as a path in an undirected graph.  
Given this trajectory representation, we can define a corridor: A (_M,S_)-corridor is a sequence of \(M\) cells in \(D^{\prime}\) such that all cells are shared by at least \(S\) trajectories in a given time slot.  
A first naive approach to detect corridors is described in Algorithm 1.  
For every trajectory, this algorithm scrolls down its elements to decide whether a pair of consecutive cells are shared by at least other \(S-1\) trajectories. If this happens, it must be checked whether the subsequent cell, together with the frequent pair of cells found, constitutes a frequent triple for at least \(S\) trajectories. This continues until you find the largest set of frequent cells.  
The complexity of this algorithm is a function of \(L\) and \(N\). The number of performed operations is \(\sum_{i=1}^{L-S+1}(N-M+1)*(L-i)*(N-M+1)*2\), which results in a complexity \(O(N^{2}L^{2})\). This complexity is to high when considering datasets with millions of data points and thousands of trajectories.