esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Limitations of _Rmr_ and \(Q\) Systems
### Limitations Related to \(Rmr\) System  
The key benefit of the \(RMR\) classification is that it is easy to practice. However, this system is comparatively insensitive to slight changes in \(RMR\) value. Taking ground behavior in consideration, adequate support measure be selected with clear understanding in tunnel design [1]. Based on ground behavior and the available tools for underground excavation in rock engineering, the application of \(RMR\) systems are the lowest among the classification systems [56], as shown in Table 8. This system is based on a comparatively small case record, which makes the \(RMR\) system less appropriate as an empirical design technique for rock-mass support, particularly when in-situ stresses or time-dependent rock mass properties are of importance for the rock engineering design [20]. In the \(RMR\) system, the rock-mass quality is divided into five classes but these classes are scarcely comparable due to the wide variation of rock-mass quality within a single class [140]. As the classification and design systems develop, the old forms of rock-mass classification systems are not always well-matched with new design approaches. Presently, usual mistakes of \(RMR\) system applications primarily deal with the use of old versions of the system [141], for example, the use of \(RMR_{76}\) for hard rock-stability analysis for a span in entry-type excavation [142]. Although this system is improved tremendously in terms of characterization and support, it is noticeable that the application of \(RMR_{89}\), \(RMR_{14}\) systems for tunnel-support design in high-stress locations is still a limitation of the system [58]. Although the positive impact of mechanical excavation is incorporated in the latest version of \(RMR\), that is, \(RMR_{14}\)[23], the mechanical excavation impact is negative in high-stress environments and drill and blast excavation has a positive impact against rock bursting [143; 144].  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Ground Behavior**}} & \multicolumn{3}{c}{**Empirical Rock-Mass Classification System**} \\ \cline{2-4}  & \(RMR\) & \(Q\) & \(RMI\) \\ \hline Stable & 2 & 2 & 1–2 \\ Fragment (s) or block (s) fall & 1–2 & 1–2 & 1–2 \\ Cave-in & 3 & 2–3 & 2 \\ Running ground & 4 & 4 & 4 \\ Buckling & 4 & 3 & 3 \\ Rupturing from stress & 4 & 3 & 3 \\ Slabbing, spalling & 4 & 2 & 2 \\ Rock burst & 4 & 3–4 & 2 \\ Plastic behavior (initial) & 4 & 3–4 & 3 \\ Squeezing ground & 4 & 3 & 3 \\ Raveling from slaking or fiability & 4 & 4 & 4 \\ Swelling ground & 4 & 3 & 3 \\ Flowing ground & 4 & 4 & 4 \\ Water ingress & 4 & 4 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The fitness of empirical rock-mass classification systems for different ground behavior during tunnel excavation.
## 4 Model Design and Interpretation  
Considering the two modal features of text and audio in speech emotion recognition, based on the TIM-Net network structure, the TAB design is improved and combined with the use of multi-head attention, and the network framework is constructed, as shown in Figure 7. The features extracted after passing through Wav2 Vec2.0 and RoBERTa have a dimensionality of 768. The multi-head attention mechanism employs 8 heads, with 2 layers in the encoder. By increasing the number of heads, the model can capture more contextual information. The TAB consists of 3 layers, with ReLU used as the activation function within the TAB. The input dimensionality received by the fully connected layer is 768 \(\times\) 2 (concatenation of audio and text), with the gelu activation function and AdamW optimizer used and a learning rate of 5 \(\times\) 10\({}^{-5}\). To prevent overfitting, the dropout is set to 0.1.  
Figure 6: Schematic diagram of the modified TAB structure.  
The first part is the feature extraction module. This section primarily focuses on extracting features from the input data, which include two modalities: audio and text. The process for extracting the features from audio modality data is as follows: first, encode the audio using Wav2 vec-2.0, and then introduce a multi-head self-attention mechanism to learn more discriminative speech emotion features. The feature extraction process for text modality data involves using the BERT model, followed by introducing a multi-head self-attention mechanism to focus on significant emotional features within the text sequence.  
The second part is the Cross-Modal Encoder (CME) attention module. This section primarily models the cross-modal interactions of the multi-modal features, utilizing a cross-modal attention mechanism to jointly optimize the feature embeddings for audio and text. The cross-modal attention mechanism achieves this by learning two sets of semantic interaction weights separately and readjusting the feature representations of audio and text. This enables capturing interactive information between the audio and text modalities, achieving semantic consistency in the multi-modal context.  
The third part is the emotion classification module. In this section, the multi-modal fusion features of audio and text are first concatenated. Then, the TAB module is employed to learn the temporal dimension features with context dependencies. The features learned with context dependencies are then put into an FC layer, utilizing a softmax classifier to obtain a probability matrix. The maximum value in the matrix is taken as the final emotion recognition result.  
The following sections will provide separate introductions to the cross-modal attention module and the emotion classification module of this model.
## 3 Materials and Methods
### Ensembles of Decision Trees  
A binary decision tree is a recursive structure containing nodes and edges. Each node represents a predicate with respect to an attribute. Depending on its value, the path taken to classify a sample continues to the left or right branch until a terminal node is reached. Every path from the root to a terminal node defines a propositional rule. Specifically, the format of a symbolic rule is given as: \"If tests on antecedents are true, then class \(C\)\", where \"tests on antecedents\" are in the form:  
* \(x_{i}\leq t_{i}\); or
* \(x_{i}\geq t_{i}\);  
with \(x_{i}\) as the \(i^{th}\) input variable (or attribute) and \(t_{i}\) as a real number. Class \(C\) designates a class among several possible classes. Figure 2 illustrates an example of a decision tree. The  
Figure 1: Transformation of an ensemble of three DTs into an ensemble of neural networks. First, from each DT, a number of rules are generated (R11, R12, etc.); second, each rule is inserted into a single neural network (NN11, NN12, etc.); third, the final classification is the result of all of the neural networks’ classifications.  
learning phase of such a model consists in determining at each node of the tree the best attribute for accurately dividing the learning samples. Many possible criteria can be used to determine the best splitting attribute; for more details, see [26; 27].  
The number of nodes in a shallow tree is very limited. It represents a \"weak\" learner with limited power of expression. For instance, a tree with a unique node performs a test only on an attribute. This type of DT is also called a decision stump. With the use of boosting techniques [4], ensembles of weak learners become strong classifiers [28] that are able to learn complex classification problems. In this work, we train ensembles of Gradient-Boosted Trees (GB) [8].  
Random Forests (RF) are ensembles of DTs [7] trained by bagging [3]. Specifically, bagging selects for each classifier included in an ensemble a number of samples drawn with a replacement from the original training set. Since many of the generated samples may be repeated while others may be left out, a certain diversity of each single predictor proves to be beneficial with respect to the whole ensemble of combined classifiers. In addition, each tree can be constrained at each induction stage to select a small proportion of the available attributes.
# Map Matching Based on Seq2 Seq with Topology Information
## 2 Related Work
### Basic Concepts and Problem Description
#### 2.1.3 Grid Dictionary  
The grid dictionary partitions the map into an N by N grid, serving as the word vector dictionary for the model. Increasing N enhances division accuracy, albeit at the expense of augmenting model parameters. The grid is arranged in ascending order from the bottom left to the top right. The dictionary takes on a sequential form of [1, 2,..., N \(\times\) N].
# UAV Path Planning Model Based on R5 DOS Model Improved A-Star Algorithm
## 2 Materials and Methods
### A-Star Algorithm  
The traditional A-star algorithm has the advantages of a simple principle and fast calculation speed. However, its spatial growth is exponential and the larger the map the greater the amount of computation. In the three-dimensional space, the traditional A-star algorithm will have a very large amount of computation, serious memory occupation, long calculation time and low pathfinding efficiency. The A-star algorithm will search for many unnecessary nodes in the process of running, resulting in a great increase in the running time of the algorithm. To solve this problem, this paper improves the A-star algorithm based on the RSDOS model and combines it with the hop-point search algorithm.  
The A-star algorithm mainly uses the evaluation function to guide and select the expansion nodes in the raster map, to traverse the nodes around the grid map each time and to calculate the evaluation function. Finally, it selects the smallest path of the evaluation function to move, as shown in Figure 2.  
We construct a cost function in the A-star algorithm to search for path nodes. The cost function \(f(n)\) can be expressed as \(f(n)=G(n)+H(n)\), where \(H(n)\) is the estimated cost function expression for the shortest path from the nth node to the target node and \(G(n)\) represents the move cost function expression for the shortest path from the start point to the nth node. So, according to the expression, it is a function expression \(f(n)\) for the shortest path between the nth node of the UAV from the starting point to the midpoint. This calculation generally uses Manhattan distance, Chebyshev distance and Origid distance. For this article, we will use the Origid distance to calculate the cost of movement. For the first three-dimensional space represented by the raster method, the cost d is shown in Equation (4).  
\[d=\sqrt{\left(x_{2}-x_{1}\right)^{2}+\left(y_{2}-y_{1}\right)^{2}+\left(z_{2}- z_{1}\right)^{2}} \tag{4}\]  
(\(x_{1}\), \(y_{1}\), \(z_{1}\)) and (\(x_{2}\), \(y_{2}\), \(z_{2}\)) represent the coordinates of nodes, respectively. \(f(n)\), \(G(n)\) and \(H(n)\) can be calculated by Equation (4).  
Figure 2: Pathfinding method of the A-star algorithm.  
We redefine the nodes of UAV searches. In three-dimensional spaces, there are many nodes that can be moved by UAVs, far more than a few atomic directions in two-dimensional spaces. According to the R5 DOS model, we divide the space into 16 regions, but this division is not conducive to the judgment of the A-star algorithm for nodes. We have improved the spatial relationship division of the R5 DOS model, focusing on the eight limits in space and establishing eight cube regions. Assuming that the orange dot is a UAV, and all the vertices of its adjacent limits, the yellow dot can be used as neighbors of the current node, that is, the next search node. Then, according to the concept, there can be a total of 26 search nodes, as shown in Figure 3.
## 3 Leak Detection Experimental Setup  
### Laboratory Tests  
A laboratory based test bench system was designed and developed in order to test the capabilities of the proposed non-invasive (to the pipe) pressure sensor assembly to detect and locate leaks. This system consists of a U shaped section made of 40 mm PVC pipe with a simulated leak in the middle section (Figure 8). Water was circulated in the system by a common water pump capable of providing up to 3 bars of pressure. A hole with a diameter of 10 mm was made in the middle section of one of the PVC pipe sections as shown in Figure 8. This hole was plugged with a rubber insert which would pop out after a random amount of time, resulting in a leak in the pipe initially, but at the time the rubber insert pops out, it behaves more like a small burst.  
Figure 8 shows five FSR sensors attached to the PVC pipe sections at 2 m intervals. Sensors 1, 2 and 3 were located before the leak and Sensors 4 and 5 were located after the leak. Each sensor was secured on the pipe with a stainless steel jubilee clip. Signals from the FSR based pressure assemblies were recorded at 100 samples per second for each sensor using a Labjack U3 data acquisition device. The high data rates for this experiment allowed the identification of the optimum sampling rate for the FSR.
# The Levenberg-Marquardt Method for Acousto-Electric Tomography on Different Conductivity Contrast
###### Abstract  
The stability and convergence performance of Levenberg-Marquardt method for acousto-electric tomography (AET) applied to different levels of conductivity contrast is studied in this paper. As a multi-physical imaging modality, acousto-electric tomography (AET) provides high spatial imaging resolution while also conserving the high contrast property of electrical impedance tomography. The Levenberg-Marquardt method is a well known iteration scheme which can be applied for the nonlinear problem of AET. However, the influence of the conductivity contrast on the stability and convergence performances of this conductivity reconstruction method is rarely discussed in the literature. In this paper, the performance of the Tikhonov regularization-based Levenberg-Marquardt method is applied to reconstruct conductivity map with different conductivity contrast between different regions of the domain of interest (DOI). Numerical investigations are carried out for phantoms with different conductivity contrast. Reconstructed results with different levels of noise are presented and discussed in detail.  
acousto-electric tomography; Levenberg-Marquardt method; conductivity contrast +
Footnote †: journal: Applied Physics Letters
## 4 Case Study
### Parameter Estimation and Result Analysis
\begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **Index Storage Life/h** & **Storage Life Assessment Value/h** \\ \hline SNADM 1 & 42,291 \\ Simple IG model 2 & 43,028 \\ IG-RE 3 & 43,359 \\ SDRD 4 & 43,401 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The average storage life of a certain type of missile tank. Figure 4: The functional relationship between stress level and product performance degradation. Based on the PDF and reliability models of each method, their PDF graphs and reliability function curves can be obtained, as shown in Figures 5 and 6. From Figure 5, it can be seen that the peak of the proposed SDRD method is higher and the range of variation is narrower. This indicates that the data distribution is more concentrated and the accuracy of its life assessment is higher. From Figure 6, it is very clear that compared to other methods, the SDRD method has a better performance in storage life assessment. However, the reliability function curves of the storage life assessment methods based on the SNADM and simple IG process models do not show significant differences. Therefore, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) are used to select the model quantitatively. Compared with the AIC, the BIC has a greater penalty for model complexity. To prevent overfitting, the BIC tends to choose models with fewer parameters. In contrast, the AIC imposes a relatively smaller penalty for complexity when selecting models. To enhance the life assessment ability of the model, AIC tends to favor models with more parameters. Based on this, this article has calculated the AIC and BIC values for four methods: SNADM, simple IG model, IG-RE, and SDRD. The formulas for the AIC and BIC are as follows:  
\[AIC=-2\times\ln L\big{(}\hat{\mathbf{\theta}}\big{)}+2\varpi \tag{27}\]  
\[BIC=-2\times\ln L\big{(}\hat{\mathbf{\theta}}\big{)}+\varpi\ln(n) \tag{28}\]  
where \(\ln L(\hat{\mathbf{\theta}})\) is the estimate of the log-likelihood function. \(\varpi\) is the number of parameters. \(n\) is the number of samples. Based on Formulas (27) and (28), the AIC values and the BIC values for each model are calculated and shown in Table 3. It can be seen from the table that the AIC and the BIC of the proposed SDRD model are the smallest. This indicates that the SDRD model has a better fitting effect on the data and reduces the risk of overfitting. It further prevents the issue of excessively high model complexity due to overly high model precision and avoids the phenomenon of the curse of dimensionality. Therefore, the SDSDDT assessment  
Figure 5: The PDF graphs for each model. Figure 6: The reliability function curves for each model. method proposed in this article, which considers random effects and detection errors, is more accurate and reasonable.
## 3 Model Development and Validation
### Material Constitutive Models  
#### Concrete Constitutive Models  
To simulate the concrete core, a concrete damage plasticity (CDP) model was utilized in which the compressive and tensile behaviors of the concrete is modeled. In this paper, these behaviors were modeled based on the characteristics defined in GB 50010-2010 [42] as follows:  
\[\sigma=(1-\mathrm{d}_{\mathrm{c}})\mathrm{E}_{\mathrm{c}}\varepsilon \tag{1}\]  
\[\mathrm{d}_{\mathrm{c}}=\left\{\begin{array}{ll}1-\frac{\mathrm{p}_{\mathrm{ c}}\mathrm{n}}{\mathrm{n}-\mathrm{p}_{\mathrm{c}}^{+}\mathrm{n}},&x\leq 1\\ 1-\frac{\mathrm{(x-1)}^{2}+x}{(\mathrm{x-1})^{2}+x},&x>1\end{array}\right. \tag{2}\]  
Figure 5: Finite element (FE) models for the SDC wall.  
where \(\rho_{\rm c}=\frac{\ell_{\rm c}}{{\rm E}_{\rm c}\,\ell_{\rm c}}\); \(n=\frac{{\rm E}_{\rm c}\,\ell_{\rm c}}{{\rm E}_{\rm c}\,\ell_{\rm c}-\ell_{\rm c}}\); \(x=\frac{\ell_{\rm c}}{\ell_{\rm c}}\); \(\sigma\) denotes the compressive stress of the concrete; \(\epsilon\) is the compressive strain of the concrete; \({\rm d_{\rm c}}\) is the axial compressive damage parameter; \({\rm f_{\rm c}}\) is the ultimate compressive strength of the concrete; and \(\varepsilon_{\rm c}\) is the compressive strain corresponding to \({\rm f_{\rm c}}\).  
\[\sigma=(1-{\rm d_{\rm t}}){\rm E}_{\rm c}\,\epsilon \tag{3}\]  
\[{\rm d_{\rm c}}=\left\{\begin{array}{ll}1-\rho_{\rm t}\Big{[}1.2-02x^{5} \Big{]},\ x\leq 1\\ 1-\frac{\rho_{\rm t}}{\alpha_{\rm t}(x-1)^{1.7}+x},\ x>1\end{array}\right. \tag{4}\]  
where \(\rho_{\rm t}=\frac{{\rm f_{\rm t}}}{{\rm E}_{\rm c}\,\epsilon_{\rm t}}\); \(x=\frac{\epsilon}{\epsilon_{\rm t}}\); \(\sigma\) denotes the tensile stress of the concrete; \(\varepsilon\) denotes the tensile strain of the concrete; \({\rm d_{\rm t}}\) denotes the axial tensile damage parameter; \({\rm f_{\rm t}}\) denotes the ultimate tensile strength of the concrete; \(\varepsilon_{\rm t}\) denotes the tensile strain corresponding to \({\rm f_{\rm t}}\); and \(\alpha_{\rm t}\) denotes the axial tensile regression parameter.  
In CDP models above, as shown in Figure 6, unidirectional stress-strain curves are required and the dilation angle is utilized to describe the friction properties and shear behavior of concrete at confining pressure. According to the reference [43], the dilation angle can be defined as \(30^{\circ}\). An additional sensitivity analysis was carried out and a limited difference of ultimate compressive resistance and ductility was found with values less than 5%. Also, a small value for the viscosity parameter \(\kappa=0.0001\) was used to improve the convergence rate in the concrete following the suggestion from [PERSON] and [PERSON] [2].
## 3 Proposed Methodology
### Weighting Schemes
#### 3.3.1 Equal Weighting Scheme  
The equal weighting scheme assigns equal importance to both \(loss_{dis}\) and \(loss_{dim}\). By simply setting \(\alpha\) to 0.5, this approach assumes that both the discrete and dimensional emotion representation tasks are equally critical for the SER model's performance. While simple to implement, this scheme might not always yield the best results. However, it allows for an initial baseline comparison against non-joint models.
# Chaos-Based Physical Unclonable Functions
###### Abstract  
The concept presented in this paper fits into the current trend of highly secured hardware authentication designs utilizing Physically Unclonable Functions (PUFs) or Physical Obfuscated Keys (POKs). We propose an idea that the PUF cryptographic keys can be derived from a chaotic circuit. We point out that the chaos theory should be explored for the sake of PUFs as a natural mechanism of amplifying random process variations of digital circuits. We prove the idea based on a novel design of a chaotic circuit, which utilizes time in a feedback loop as an analog continuous variable in a purely digital system. Our design is small and simple, and therefore feasible to implement in inexpensive reprogrammable devices (not equipped with digital clock manager, programmable delay line, phase locked loop, RAM/ROM memory, etc.). Preliminary tests proved that the chaotic circuit PUFs work in both advanced Field-Programmable Gate Arrays (FPGAs) as well as simple Complex Programmable Logic Devices (CPLDs). We showed that different PUF challenges (slightly different implementations based on variations in elements placement and/or routing) have provided significantly different keys generated within one CPLD/FPGA device. On the other hand, the same PUF challenges used in a different CPLD/FPGA instance (programmed with precisely the same bit-stream resulting in exactly the same placement and routing) have enhanced differences between devices resulting in different cryptographic keys.  
Keywords:physically unclonable function; chaos theory; chaotic circuit; FPGA; CPLD; challenge-response authentication; hardware security; side-channel attacks; cryptographic keys +
Footnote †: journal: Applied Sciences