esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
[10] analyzed the relationship between rock yield strength, ultimate strength, residual strength, and confining pressure and obtained rock's peak shear strength parameters and residual shear strength. According to the theory of elastic-plastic mechanics, a bilinear elastic-linear softening-residual ideal plastic model considering the strain-softening of rock was established, the constitutive equations were set up, and the parameters of each stage were determined. [PERSON] et al. [11] analyzed the change law of damage dissipation energy in the relaxation process, established the relaxation damage evolution equation, introduced the damage factors into the Nishihara model, established a nonlinear relaxation damage model, and verified the rationality of the established model. [PERSON] [12] studied the mechanical properties of shale. [PERSON] [13] studied the strength and failure criteria of full-size rocks. [PERSON] et al. [14] experimentally analyzed the influence of stress on the brittleness of mud shale and concluded that, with an increase in confining pressure, its elastic and plastic strain increased, and its brittleness index decreased. [PERSON] et al. [15] studied the compressibility of shale gas reservoir rocks. [PERSON] et al. [16] analyzed mudstone shaft wall collapse, and the results show that water weakened the strength of the mud shale. According to the study of [PERSON] et al. [17], under uniaxial compression, the peak strength and elastic modulus of sandstone gradually decrease with the increase in water content, while the change in peak strain shows an opposite trend. The total work and elastic energy decreased with the increase in water content. [PERSON] et al. [18; 19] characterized the micromechanical properties of shale after it encountered water, and a new water-sensitivity-based upscaling model was constructed. [PERSON] et al. [20] evaluated the shale fluid sensitivity of target unconventional shale formations by conducting two shale fluid sensitivity tests on samples using four different water-based fluids, as well as fresh water and diesel oil. However, there are few experimental studies on the mechanical properties of shale under high confining pressure and water-rock coupling conditions. In this paper, triaxial tests of water-sensitive shale under 60 MPa, 70 MPa, and 80 MPa confining pressure and different water contents are carried out. The damage and failure modes of shale under different confining pressures and different water contents are discussed, and the changes in its mechanical properties, residual strength, and softening coefficient are analyzed. The experimental results are of great value for guiding the exploration and development of oil and gas in our country and for other engineering projects related to water-sensitive rocks under high confining pressure.
## 4 Discussion
Interestingly, it has been suggested that HMGCR participates in OA pathogenesis, given that its expression is significantly higher in chondrocytes from OA patients [73; 74]. Long-term use of statins (HMGCR inhibitors) also significantly reduces the risk of OA among middle-aged adults with cardiovascular diseases [75]. Therefore, the presence oftocopherol in PT3 may hinder the HMGCR-suppressing effects of \(\delta\)-tocotrienol, which may explain the absence of self-repair and anabolic activities in PT3-MIA co-treated chondrocytes. Further study is required to confirm our speculation and the role of HMGCR in OA initiation, chondrocyte damage and repair mechanisms. Overall, the current study proposes that AnTT and PT3 prevent the reduction in cell viability of chondrocytes caused by MIA, probably through lowering oxidative stress. AnTT also enhanced the self-repair mechanism of chondrocytes exposed to MIA by activating their anabolic activities, as evidenced by increased COL2\(\alpha\)1/COL1\(\alpha\)1 ratios, SOX9, COL2\(\alpha\)1 and aggrecan levels. PT3 does not share the self-repair activation mechanism possessed by AnTT. A summary of the mechanism of actions of AnTT and PT3 on chondrocytes stimulated with MIA is presented in Figure 6.  
Several limitations in this study should be addressed. First, the findings of the current study should be validated in more than one chondrocyte cell line and primary mammalian chondrocytes. Second, cartilage explants should be considered, because chondrocytes might behave differently in monolayer and three-dimensional constructs. Third, the enhanced self-repair mechanism of chondrocytes exposed to AnTT and MIA concurrently should be scrutinised, because the current evidence is preliminary. Some studies have reported an adaptive increase in antioxidant response of chondrocytes exposed to MIA [34; 76]. Forth, silencing of SOX9 could be performed to determine its role in mediating the action of AnTT in promoting chondrocyte repair. Fifth, the ELISA results should be confirmed with a gene expression study or Western blot analysis. Lastly, the chemical interaction between MIA or ROS with AnTT and PT3 should be examined to confirm their upstream protective mechanisms. Nevertheless, the current study suggests the novel action of tocotrienols, especially AnTT, in counteracting chondrocyte damage due to MIA.
###### Abstract  
An often-utilized solution in terms of providing support to underground excavation, the fully grounded rebar rock bolt system presents optimization potential due to existing technological limitations in capturing and understanding its composite system response. In order to address these limitations, the development and application of Distributed Optic Fiber Sensing (DOS) allows for continuous strain monitoring (at a spatial resolution of 0.65 mm utilizing the technique defined herein) across a full spectrum of loading. A robust laboratory investigation was conducted featuring 24 rock bolt specimens. This examined the effects of two selected independent variables: rib spacing (from 13 mm to 68 mm) and grout annulus (from 7.7 mm to 22.8 mm). This body of research provides valuable insight into the performance of grouted rebar rock bolts and the effects of the selected parameters (rib spacing and size of the grout annulus), while also highlighting an advanced monitoring technique. Results indicated that rib spacing was a negative predictor of bond performance. No definite conclusions were drawn in terms of the effect of the size of grout annulus; however, findings provide limited support for an optimal sizing in relation to rib height. The results were also compared to analytic and numerical models. These insights can aid in calibrating and validating numerical models, and improve monitoring and rock bolt design within the overall goal of improving and optimizing ground support design arrangements.  
fiber optic technology; fully grouted rock bolts; load transfer mechanics; rebar profile; rib spacing; grout annulus; stress distribution; critical embedment length Article
## 2 Materials and Methods
### Texture Feature Extraction
#### 2.3.2 Haralick Features  
The Haralick features are based on the gray level co-occurrence matrix (GCLM) [31], which measures the number of times a specific intensity pattern between adjacent pixels is repeated. Since adjacency can be measured in four principal directions (e.g., vertical, horizontal, and two diagonal directions), the GLCM is computed using four angles: \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), and \(135^{\circ}\). The Haralick features mathematically describe the GLCM through the calculation of the symmetry, contrast, homogeneity, entropy, energy, and correlation. These features are computed for four directions, so a total of 24 descriptors are extracted for each ROI. The mathematical description of these features can be found in Table 3.
# Biophotonics in Dentistry
## 3 Applications in Dentistry
### Lasers  
Laser was first used in dentistry in the 1960s, and since then, applications have developed massively, rapidly allowing for greater efficiency, ease and comfort in treatments. Laser refers to light amplification by stimulated emission of radiation; concentrated, focused and coherent monochromatic radiation is emitted in single wavelength. When interacting with tissue, this radiation may be transmitted, absorbed, scattered or reflected depending on the optical properties of the tissue and the wavelength of the laser. Various effects may be achieved, including photoablation, photochemical effects, fluorescence and vaporisation. These can be utilised for a vast number of dental applications. Photoablation and vaporisation are used to remove tissue; fluorescence can be used to detect caries; and the photochemical effect may be used to initiate chemical reactions, such as curing of composite resin [121]. Lasers can be classified based on level of penetration, such as deep of superficial; wavelength; or application, such as soft tissue and hard tissue lasers.
# Marine Internet for Internetworking in Oceans: A Tutorial
## 2 Overview of Marine Internet
### Typical Applications
#### 2.2.2 Enhancement of Maritime Radio Systems  
The maritime communication system is an important communication platform for maritime transportation, safety and rescue operations in oceans. The current system is mainly composed of shore-based maritime radio systems and maritime satellites. Similarly, the price of maritime satellite services is still very high [21], which increases the cost of maritime transport. The shore-based maritime radio, though mature in development, is unable to meet the increasing communication demands of maritime transportation itself due to the bandwidth limitation as discussed in Section 4.1. Thus, it cannot support the new development of intelligent shipping that can greatly improve the efficiency and safety of maritime transport to reduce the cost of maritime logistics. Therefore, it is necessary to develop new maritime communication systems with larger capacities and higher cost-effectiveness. As discussed in Section 5.1, marine Internet can make this happen because it can make the best use of various communication resources available in oceans.  
Figure 1: Overview of marine Internet.
# Analytical Model for Information Flow Management in Intelligent Transport Systems
## 5 Discussion  
The introduction of the information commutation, or digital, technologies into the modern reality of transport systems operation allows for the parallel processing of multiple \"inputs\" and \"outputs\" within intelligent transport systems [74; 75; 76; 77; 78; 79] and ensures adequate amounts, or a \"database\", of optimization parameters [80; 81; 82; 83; 84; 85; 86; 87; 88; 89]. Using the proposed analytical model, it is possible to equip the ITS management system with an information analysis platform that builds on a synthesis of methods for managing the complex systems' structural dynamics and allows for optimal correspondence of the information situation to the decision making. In this model, the formation of the candidate decisions space and the division of the information situation into levels and subsystems based on the principles of technology homogeneity and information state heterogeneity, are achieved through an object-oriented approach. In this way, the essence is revealed of the process of assigning to the general ITS model the specific contents that meet all pre-set conditions, economic feasibility requirements, and efficiency standards. It should be noted here that the proposed ITS modeling principles have a dual nature:  
1. On the one hand, when the task is to provide the state forecast, any object or process should be considered as an organized, dialectically developing system.
2. On the other hand, when the task is to analyze this system for structural arrangement, properties, and internal and external interactions with the environment, a multidimensional study is required - the one that will provide an in-depth knowledge and description of the system's current state as a prerequisite of problem solving.  
This duality is not a drawback and reinforces the relevance of the systems approach in dealing with innovative developments. The proposed approach has been used by the authors to create an analytical model of ITS management that allows the tracing of the variables over time for a particular process from the perspective of multivariate forecasting. The decision-making simulation tools used in this model can be modified in relation to a particular process depending on the information situation existing for the given conditions of transportation.  
When developing a management routing method, the task is to provide not only the descriptions of the objects and criteria of multicriterion systems, but also methods for transforming attributes that are essential for deriving structures for more complex system states and making the management process more flexible and versatile.  
The proposed method offers the analytical tools allowing the decision-making process to be effective under stochastic uncertainty, i.e., in conditions where the information needed for the choice of the (normal, lognormal, etc.) random variable distribution law is limited.
# MIMO Channel Estimation in an SDR Platform for Evaluation of D&F Relay Nodes
## 4 Channel Estimation for the Core Network: Link eNB-to-Relay Node
### Linear Equalizers  
After estimating \(\mathbf{H}\), the channel effects are equalized and the noise in the received RG (\(\mathbf{Y}\)) is reduced. In this sense, the MMSE and ZF equalizers are applied, as shown in Figure 3. In the following subsections, the MMSE and ZF equalizers are detailed.
## 2 Principles and Methods
### High-Precision and High-Resolution SST via Time-Frequency Instantaneous Phases (HSST)
\tag{16}\]  
then, estimated high-precision time-frequency instantaneous frequencies can be given by  
\[V_{6}(t,f)=\frac{1}{2\pi}\frac{\Delta\theta_{2}(t,f)}{\Delta t} \tag{17}\]  
and corresponding SST results can be expressed as  
\[S_{4}(t,v)=\int G(t,f)df,if\quad V_{6}(t,f)\in[v-\Delta v,v+\Delta v] \tag{18}\]  
which is considered HSST; HSST can avoid the impacts of the time-sample interval and period-jumps of \(\theta_{1}(t,f)\) to obtain high-precision and high-resolution SST results with high-efficiency. Figure 2a shows the time-frequency principal instantaneous phases of the 50 Hz harmonic signal; Figure 2b,c show the time-frequency instantaneous amplitudes obtained by PSST with \(\Delta t\) = 2 and 4 ms, respectively; Figure 2d,e show the time-frequency instantaneous amplitudes obtained by HSST with \(\Delta t\) = 2 and 4 ms, respectively. Table 2 shows the time consuming of PSST and HSST for the 50 Hz harmonic signal with 4 s length. Figure 2: \(Cont.\)  
Comparison of Figures 1 and 2 shows that PSST has higher precision and resolution than MSTFT and CSST, except for time-points corresponding to period-jumps of time-frequency principal instantaneous phases; HSST avoids period-jumps during PSST, and it has higher precision and resolution than MSTFT, CSST, and PSST.
## 2 Materials and Methods
### Study Selection  
The PICOS criteria (Participants, Interventions, Comparisons, Outcomes, and Study design) were applied to define the research question and determine study eligibility: (1) Participants: Adults with MS, aged 18 or older; (2) Interventions: Core training-based interventions alone or combined with other interventions, being the core training the primary focus; (3) Comparisons: No intervention, placebo, or other interventions that did not include core training; (4) Outcomes: Measures of gait (e.g., timed up and go or 10-m walk test) or exercise capacity (e.g., 6 MWT); (5) Study design: Randomized controlled trials. Studies published in English, Spanish, and French were included.  
A total of 781 records were identified. After removing duplicates using EndNote bibliographic management software (n = 136), 645 titles and abstracts were screened, and 614 were excluded for not meeting the inclusion criteria. Then, the full text of 31 articles was reviewed, and 12 articles met all the eligibility criteria and were included in the final analysis. Two reviewers ([PERSON]-[PERSON] and [PERSON]-[PERSON]) independently conducted the search and study selection. Disagreements were resolved by consulting a third reviewer ([PERSON]-[PERSON]).
## 3 Results
 \tag{1}\]  
where: \(dX_{P}\), \(dY_{P}\), \(dZ_{P}\), and \(dX_{Q}\), \(dY_{Q}\), \(dZ_{Q}\) are corrections of the approximate coordinates \(X_{P}\), \(Y_{P}\), \(Z_{P}\) and \(X_{Q}\), \(Y_{Q}\), \(Z_{Q}\) of the points \(P\) and \(Q\), respectively, \(v\) is the random error of distance measurement s with zero value expectation and known standard deviation equaling \(\sigma_{s}\) = 1 mm, \(s_{0}\) is an approximate value of the measured distance s:  
\[s_{0}=\sqrt{\left(X_{Q}-X_{P}\right)^{2}+\left(Y_{Q}-Y_{P}\right)^{2}+\left(Z_ {Q}-Z_{P}\right)^{2}} \tag{2}\]  
The Gauss-Markov observational model [21; 25] for all measured spatial distance observations \(s_{1}\), \(s_{2}\),..., \(s_{n}\), \(n\) = 40, (Figures 1 and 2) is completed regarding the Equation (3):  
\[\mathbf{v}=\mathbf{Ax}-(\mathbf{s}-\mathbf{s}_{0}),\mathbf{\Sigma}_{\mathbf{s}} \tag{3}\]  
where: \(x\)--vector of unknown corrections: \(dY_{2}\), \(dX_{3}\), \(dY_{3}\), \(dZ_{3}\), \(dX_{4}\), \(dY_{4}\), \(dX_{5}\), \(dY_{5}\), \(dZ_{5}\),..., \(dX_{12}\), \(dY_{12}\), \(dZ_{12}\), \(v\)--vector of unknown observational error residuals \(v_{1}\), \(v_{2}\),..., \(v_{n}\), A-known design matrix, s--vector of observations \(s_{1}\), \(s_{2}\),..., \(s_{n}\), \(s_{0}\)--vector of approximate values of observations \(s_{0,1}\), \(s_{0,2}\),..., \(s_{0,n}\), \(\sigma_{s}\)--diagonal covariance matrix of observations s, composed from standard deviations \(\sigma_{s,1}\), \(\sigma_{s,2}\),..., \(\sigma_{s,n}\). In the adjustment, the network is connected with the reference frame by three coordinates, \(X_{I}\), \(Y_{1}\), \(Z_{1}\) of point 1, two coordinates \(X_{2}\), \(Z_{2}\) of point 2, and one coordinate \(Z_{4}\) of point 4. These coordinates are constant; it means \(dX_{1}=dY_{1}=dZ_{1}=0\), \(dX_{2}=dZ_{2}=0\), \(dZ_{4}=0\). The least-squares solution of the Gauss-Markov model (8): \(v^{T}\sum_{s}^{-1}v=min\) is given by well-known formulas [21; 26]:  
\[\mathbf{x}=\mathbf{\Sigma}_{\mathbf{x}}A^{T}\mathbf{\Sigma}_{\mathbf{s}}^{-1}(\mathbf{s}-\mathbf{s}_{0}), \tag{4}\]  
where the equations:  
\[\mathbf{\Sigma}_{\mathbf{x}}=\left(A^{T}\mathbf{\Sigma}_{\mathbf{s}}^{-1}A\right)^{-1} \tag{5}\]  
\[\mathbf{\Sigma}_{\mathbf{v}}=\mathbf{\Sigma}_{\mathbf{s}}-A\mathbf{\Sigma}_{\mathbf{x}}A^{T} \tag{6}\]  
represent the covariance matrices of \(x\) and \(v\), respectively. Figure 3: Exemplary spatial network. The maximal value of the standard deviations \(\sigma_{X},\sigma_{Y},\sigma_{Z}\) of the adjusted coordinates \(Y_{2}+dY_{2}\), \(X_{3}+dX_{3}\)\(Y_{3}+dY_{3}\), \(Z_{3}+dZ_{3}\), \(X_{4}+dX_{4}\), \(Y_{4}+dY_{4}\), \(X_{5}+dX_{5}\), \(Y_{5}+dY_{5}\), \(Z_{5}+dZ_{5}\)... \(X_{12}+dX_{12}\), \(Y_{12}+dY_{12}\), \(Z_{12}+Z_{12}\), taken from diagonal elements of the covariance matrix (5) is equal to 1.2 mm. The adjusted positions of the network point 1, 2,..., 12 are used as so-called control points for the determination of the orientation and distortion parameters of the left and right stereo photos in the object reference frame (\(X\), \(Y\), \(Z\)).
# Docking Control of an Autonomous Underwater Vehicle Using Reinforcement Learning
## 5 Results
### Simulation Framework
While the number of neurons was selected for optimal docking performance (out of 25, 50, 100 and 200 neurons for a single seed value), the network layout and number of layers were adopted from recommendations by Mathworks. A fully connected layer employs a linear function to multiply the input by a weight matrix and then add a bias vector. A ReLu layer employs the rectified linear unit activation function, which is the most popular type of activation function in deep neural networks nowadays. A tanh layer, which employs the hyperbolic tangent activation function, was used to constrain the action output to the range \((-1,1)\), which was then scaled back up to the desired magnitude by a linear layer. The adaptive learning rate optimisation algorithm Adam was used for training the neural networks [54]. The learning rate was set at 0.001 with a gradient threshold of 1 for both critic networks, while the actor network had a learning rate of 0.0001. The reader is referred to [55] for greater information. Batches of 128 data samples were used for experience replay during learning. An experience buffer length of \(10^{6}\) points was set to reduce the memory cost. The discount factor was set to \(\gamma=0.99\) to favour learning of long-term reward. For the DQN algorithm, the initial exploration rate was set to \(\epsilon_{0}=1\) with a decay of 0.0001 and a minimum value of \(\epsilon_{\text{min}}=0.01\). For the DDPG algorithm, the initial variance was set to 0.3 with a decay of 0.0001. The smoothing factor was set to \(\tau=0.001\). The reinforcement learning and deep learning toolboxes of MATLAB were used to model the RL agents and deep neural networks. Figure 6: Diagram of the neural network used to approximate the action-value function by the critic of the DQN algorithm.
## 2 Related Works  
### State-Space Models  
State-space models (SSMs) provide a versatile and flexible approach for modeling sequential data [11]. Stemming from [PERSON]'s pioneering work, SSMs were rapidly employed including estimating the trajectory of the spacecraft transporting humans to the moon [12]. SSMs primarily serve as generative models for sequential data, encompassing prediction modeling, state inference, and representation learning. As depicted in Formula (1), the state-space model consists of state transition equations and observation equations. The former describes the dynamics of hidden states over time \(p(z_{t}|z_{t-1},u_{t})\), while the latter summarizes the conditional probability distribution of observations given the hidden states \(p(x_{t}|z_{t},u_{t})\).  
\[\begin{array}{l}x_{t}=A_{t}z_{t}+B_{t}u_{t}+b_{t}+v_{t}\varepsilon_{t}\\ z_{t}=\mathrm{C}_{t}z_{t-1}+D_{t}u_{t}+g_{t}\varepsilon_{t}\end{array} \tag{1}\]  
where the input vector, hidden state vector, and output vector are denoted as \(u\in\mathbb{R}^{N}\), \(z_{t}\in\mathbb{R}^{L}\) and \(x_{t}\in\mathbb{R}^{M}\). The state transition equation includes the state transition matrix \(\mathrm{C}_{t}\in\mathbb{R}^{L\times L}\) and the intensity of state transition noise \(g_{t}\), while the observation equation comprises the weight and bias of the observation model \(A_{t}\in\mathbb{R}^{M\times L}\) and \(b_{t}\), and the intensity of observation noise \(v_{t}\). Furthermore, \(B_{t}\in\mathbb{R}^{L\times N}\) and \(D_{t}\in\mathbb{R}^{M\times N}\) represent the effects of input on hidden variables and observations, respectively. The state vector follows a Gaussian distribution \(z\ \sim\ N(\mu,diag(\sigma^{2}))\), and the noise follows a standard normal distribution \(\varepsilon_{t}\ \sim\ N(0,1)\).
# A Framework for Fall Detection Based on OpenPose Skeleton and LSTM/GRU Models
## 3 Model Evaluation and Results
### Model Evaluation Index
#### 3.2.3 Accuracy  
Accuracy refers to the proportion of the primary and secondary classification targets that are correctly judged in all classified samples. Like all events, the model correctly predicts the proportion of falls and non-falls.  
\[A=\frac{TN+TP}{TP+TN+FP+FN}\times 100\% \tag{23}\]