esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The discrete element method (DEM) can be effectively used in investigations of the deformations and failures of jointed rock slopes. However, when to appropriately terminate the DEM iterative process is not clear. Recently, a displacement-based discrete element modeling method for jointed rock slopes was proposed to determine when the DEM iterative process is terminated, and it considers displacements that come from rock blocks located near the potential sliding surface that needs to be determined before the DEM modeling. In this paper, an energy-based discrete element modeling method combined with time-series analysis is proposed to investigate the deformations and failures of jointed rock slopes. The proposed method defines an energy-based criterion to determine when to terminate the DEM iterative process in analyzing the deformations and failures of jointed rock slopes. The novelty of the proposed energy-based method is that, it is more applicable than the displacement-based method because it does not need to determine the position of the potential sliding surface before DEM modeling. The proposed energy-based method is a generalized form of the displacement-based discrete element modeling method, and the proposed method considers not only the displacement of each block but also the weight of each block. Moreover, the computational cost of the proposed method is approximately the same as that of the displacement-based discrete element modeling method. To validate that the proposed energy-based method is effective, the proposed method is used to analyze a simple jointed rock slope; the result is compared to that achieved by using the displacement-based method, and the comparative results are basically consistent. The proposed energy-based method can be commonly used to analyze the deformations and failures of general rock slopes where it is difficult to determine the obvious potential sliding surface.  
rock slopes; deformations and failures; discrete element method (DEM); energy under gravity; time-series analysis Article  
An Energy-Based Discrete Element Modeling Method Coupled with Time-Series Analysis for Investigating Deformations and Failures of Jointed Rock Slopes  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], R. An Energy-Based Discrete Element Modeling Method Coupled with Time-Series Analysis for Investigating Deformations and Failures of Jointed Rock Slope. _Appl. Sci._**2021**, _11_, 5447. [[https://doi.org/10.3390/app11125447](https://doi.org/10.3390/app11125447)]([https://doi.org/10.3390/app11125447](https://doi.org/10.3390/app11125447))  
12 May 2021  
1
## 6 FIFO and LRU Caching Analysis with Data of Different Sizes  
The previous results apply to the cache support of CPU/GPU processing in a fixed, unit size data format, whereas web caches are storing files or data chunks of varying sizes. Therefore, we finally regard data objects \(O_{k}\) of different sizes \(s_{k}\). Then, the cache size \(M\) does not refer to the number of objects, but it is measured in bytes, and there is no 1:1 replacement of the data. Often, the caching strategy must perform several evictions until enough space for the new object becomes available. Small objects may be inserted without eviction into free cache space, or several evictions may be required to insert a large object.  
To the best of the authors' knowledge, hit ratio solutions for objects of varying sizes are rarely addressed in the literature and are thus open for future study. An extended Che approximation for LRU caches is proposed in Section 3.2 of [28] without further evaluation. We first summarize extended exact solutions for LRU and for the product form in Sections 6.1 and 6.2. In Sections 6.3-6.5, we extend the FIFO approximation for varying data sizes with regard to unused cache space (UCS) as an additional source of deviations.
# Split-Based Algorithm for Weighted Context-Free Grammar Induction
## 1 Introduction  
The task of grammar or automata induction is a part of symbolic artificial intelligence [1] and is called grammatical inference or grammar induction [2]. Among different subtasks of this scientific field, learning (stochastic or more general weighted) context-free grammars (CFGs) from input data has been growing in importance, due to its practical implications such as natural language and biological sequences modelling.  
Learning CFG is known to be a hard task and notable open questions are still open [2]. According to [PERSON]'s theorem [3], CFGs cannot be learned from positive examples only, but in 1969 [PERSON] proved that for effective probabilistic/stochastic CFG (PCFG) induction no negative evidence is obligatory [4]. It is imperative to note that learning PCFG only from positive data leads to grammars, thereby making it difficult to discriminate negative sequences from the input data. To overcome these difficulties, we have recently proposed the novel algorithm for weighted CFG (WCFG) learning [5, 6]. Weighted Grammar-based Classifier System (WGCS) is one of the few grammatical inference approaches learning both grammar structure (i.e., rules) and stochastic grammar parameters (i.e., weights of rules). Initially, the method was dedicated to learning crisp context-free grammar [7], and later, it was extended to weighted versions (including fuzzy one [8] or stochastic [9]).  
WGCS is learned in an unsupervised manner from unannotated data such as, a structured corpus or treebank. There are some other unsupervised grammatical inference methods like ABL [10], EMILE [11], ADIOS [12], or LS [13]. However, none of these methods induces both structure and parameters of grammar.  
The main contribution of this paper is to define and test a new version of WGCS approach, in which the split concept has been employed to reveal the grammar structure. Although the split was used for the first time in [6], its verification was rudimentary and limited due to the unrepresentative bioinformatics dataset. Subsequently, a new approach was formalised and tested over a comprehensive set of artificial CFG datasets, and itscomputational complexity was given. Moreover, the improved WGCS was compared with two state-of-the-art unsupervised methods--LS [13] and ADIOS [12]--dedicated to CFG learning. Additionally, the rule weight estimation algorithm was improved by mitigating unbalanced data bias.  
The rest of the paper is organised as follows. Section 2 gives some details about our approach. In Section 3, we present a test environment and eventually the results are reported in Section 4. Section 5 concludes the paper.
# Performance Comparisons of Broadband Power Line Communication Technologies
## 2 Structures of PLC
### IoT PLC
#### 2.1.2 Interleavers  
Subblocks \(A,B,Y_{1}\), and \(Y_{2}\) have their own subblock interleavers, which shuffle input bits within each subblock. The interleaver disperses corrupted bits in succession, thus burst bit errors are changed to random errors. The interleaved subblocks for \(A,B,Y_{1}\), and \(Y_{2}\) are denoted as \(A^{\prime}\), \(B^{\prime}\), \(Y_{1}^{\prime}\), and \(Y_{2}^{\prime}\), respectively. Since turbo code works best for the random errors [31; 32], the more dispersion there is, the better the interleaver. The bit selection block next to the subblock interleaver rearranges \(Y_{1}\) and \(Y_{2}\) once again: bits in \(Y_{1}^{\prime}\) are allocated to the odd positions in the output, and bits in \(Y_{2}^{\prime}\) are allocated to the even positions. The outputs of the bit selection block for parity blocks \(Y_{1}^{\prime}\) and \(Y_{2}^{\prime}\) are denoted as \(W_{1}^{\prime}\) and \(W_{2}^{\prime}\).  
Figure 3 shows the subblock interleaver and bit selection test inputs and their outputs for \(N=1440\) as an example. For visualization, integer numbers instead of binary numbers are assigned to subblock inputs. The subblock \(A\) has linearly increasing positive numbers, while \(B\) has decreasing negative numbers. In the same way, \(Y_{1}\) and \(Y_{2}\) are assigned but with larger and smaller slopes than \(A\) and \(B\), respectively. The horizontal axis represents the sequence position and the vertical axis the value. The shuffled subblock \(A^{\prime}\) has only positive values, which means that \(A\) is interleaved within the subblock. This also applies to subblock \(B\). However, for the subblocks \(Y_{1}\) and \(Y_{2}\), the outputs have both positive and negative values, which means that they are mixed together.  
The channel interleaver further rearranges the input stream to improve the error correction capability of the CTC encoder by changing burst errors to random errors. Note that the channel interleaver works for all input data instead of subblocks. All bits from the four subblocks are successively written to a channel interleaving matrix in columns, and the bits of the matrix are read in rows. Row-by-row readings are done not continuously, but at regular intervals to separate the input bits as much as possible. The interleaver matrix has sizes of 720 \(\times\) 8 for \(N=1440\) and \(24\times 8\) for \(N=48\).  
Figure 4 shows the channel interleaver test input and corresponding output for \(N=1440\). For visualization, integer numbers instead of binary numbers are assigned for the inputs. The input has linearly increasing positive numbers. The horizontal axis represents the data sequence position and the vertical axis represents the value. From the channel interleaver output stream, the input data are dispersed evenly, thus the adjacent bits for each bit are placed as far apart as possible.  
Figure 3: Subblock interleaver and bit selection block: (**a**) test input; and (**b**) output.
# Estimation of Aircraft-Dependent Bumpiness Severity in Turbulent Flight
## 3 Experiments and Analysis  
### Analysis of UVLM  
#### 3.1.1 Grid Refinement  
The performance of UVLM was tested as a foundation for the acceleration response analysis. The non-planar UVLM requires a reasonable grid division (or grid resolution) to achieve aerodynamic performance that is not related to lattice number. If the grid resolution is low, the vortex rings may not fit the mean camber surface well, thus leading to low accuracy. On the other hand, too high grid resolution will greatly increase the computation cost. Too small vortex lattices would also make the collocation points too close to the vortex filament as shown in Figure 3, which also reduces the computation accuracy. Taking the Boeing 737-800 aircraft as an example in this paper, the grid resolution of UVLM was first studied to improve the accuracy on the premise of computing efficiency assurance.  
The geometric and aerodynamic parameters of B737-800 aircraft are listed in references [37; 38]. According to the geometry and airfoil parameters of the wing and horizontal tail, a non-planar vortex lattice model was built, in which different surface grids of increasing mesh density were generated as shown in Table 1. The changes of the lift, drag, and pitching moment coefficients were presented as a function of the grid refinement level. The experiments show that the results of the eighth refinement level are within 1% of the values obtained with the most refined grid. It means that a satisfactory accuracy was obtained by using a grid with 40 columns, 20 rows on the wing and 12 columns, 6 rows on the horizontal tail.
# Investigation of the Mechanical Behavior of Polypropylene Fiber-Reinforced Red Clay
## 3 Results and Discussion  
### Shear Strength of Fiber-Reinforced Red Clay  
#### 3.1.1 Effect of Fiber Content on Strength  
Figure 2 shows the influence of \(FC\) on the strength of the reinforced red clay with a cell pressure of 200 kPa. As shown in the figure, the specimens with different \(FL\) exhibited basically consistent stress-strain curves, but the fiber-reinforced and plain red clay specimens exhibited completely different stress-strain curves. The axial strain of reinforced clay specimens increased as the deviatoric stress increased but exhibited no peak, whereas for the plain clay specimen, the axial strain first increased, then peaked and gradually decreased. This finding indicates that the fiber-reinforced red clay was characterized by strain hardening, which became more notable as \(FC\) increased. As shown in Figure 2c, if the ultimate strain is defined as 9%, the peak strength of the plain red clay was 403 kPa; the specimens reinforced with 0.15% and 0.80% 19 mm fiber filaments had strengths of 671 and 1055 kPa, or improvements of 66.5% and 161.8%, respectively. If the ultimate axial strain is defined as 15%, the reinforced red clay specimens with the five different \(FC\) had strengths of 755, 832, 915, 1030, and 1259 kPa, equal to 1.87 to 3.12 times the strength of the plain red clay. This result clearly indicates that the addition of fiber could improve the strength of clay specimens. These findings are in agreement with the results reported by [PERSON] et al. [10], where unconfined compressive strength tests were conducted.
# Software-Defined NB-IoT Uplink Framework--The Design, Implementation and Use Cases
## 5 Uplink Implementation
### Mapping to Physical Resources  
In general, the process of mapping transmitted data to the physical resources is based on arranging symbols within the resource grid presented in Figure 5. The given procedure should involve the current number of active subcarriers and map the symbols slot by slot, including the number of NPUSCH repetitions \(M_{rep}^{NPUSH}\). It is important to mention that within the slot for seven symbols, both NPUSCH and DMRS data should be arranged.
# Linear and Nonlinear Modelling of the Usable Area of Buildings with Multi-Pitched Roofs
## 3 Results
### Linear and Nonlinear Models Based on the Existing Buildings in Koszalin
#### 3.3.1 Linear Regression Based on Dataset of Existing Buildings  
Table 5 presents the results of the estimation of the parameters of the best-fit linear regression model, form (1), without insignificant variables and without six outliers (explaining slightly more than 10% of the data).  
The linear multiple regression model has a high degree of fit to real data. The coefficient of determination is \(R^{2}\) = 0.919 and its adjusted value is slightly lower (Adj. \(R^{2}\) = 0.915), despite the fact that 12.5% of the data were considered outliers. This means that the model reliably explains about 92% of the actual dependence of the usable area of a building on its covered area and the number of storeys.  
Both model independent variables (\(A_{C}\) and \(S_{N}\)) explain the dependent variable (\(A_{U}\)) to a similar extent, as shown by \(\beta\) weights of 0.745 and 0.661, respectively. A detailed analysis of outliers showed that the rejected observations were indeed unusual for the adopted dataset. Some of them had a very large usable area or some did not have a garage or had an unusual roof shape. Thus, the empirical formula obtained from the estimation of the parameters of the linear multiple regression model takes the following form:  
\[A_{U}=-108.357+\ 0.829\cdot A_{C}+86.748\cdot S_{N} \tag{7}\]  
For a similar set of market data, Formula (7) may facilitate estimating the value of usable area on the local market.
## 3 Experiment  
### Quantitative Evaluation Index  
To quantitatively compare the reconstructed effects of the different reconstruction algorithms from sparse-view projections, three indexes, namely structural similarity (SSIM), peak signal to noise ratio (PSNR), and root mean squared error (RMSE), are used for quantitative comparative analysis [32]. The equations of the three indexes are shown in Equations (11)-(13).  
\[SSIM(f_{A},f_{B})=\frac{(2\mu_{f_{A}}\mu_{f_{B}}+c_{1})(2\sigma_{f_{A}f_{B}}+c_ {2})}{(\mu_{f_{A}}^{2}+\mu_{f_{B}}^{2}+c_{1})(\sigma_{f_{A}}^{2}+\sigma_{f_{B}}^ {2}+c_{2})} \tag{11}\]  
where \(f_{A}\) is the reference image and \(f_{B}\) is the reconstructed image. \(\mu_{f_{A}}\) and \(\mu_{f_{B}}\) are the mean values of \(f_{A}\) and \(f_{B}\). \(\sigma_{f_{A}}\) and \(\sigma_{f_{B}}\) are the standard deviations of \(f_{A}\) and \(f_{B}\). \(\sigma_{f_{A}f_{B}}\) is the covariance between \(f_{A}\) and \(f_{B}\). \(c_{1}\) and \(c_{2}\) are constants with a small value, which are used to maintain stability. In this paper, we set \(c_{1}=c_{2}=0.001\). SSIM is used to measure the similarity between two images. A larger SSIM value indicates a higher similarity and a smaller structural difference between the two images.  
\[PSNR=10\log_{10}\frac{(\max(f_{\text{A}}))^{2}}{\frac{1}{M\times N}{\sum_{i=1} ^{M}{\sum_{j=1}^{N}{\left(f_{\text{A}}-f_{\text{B}}\right)}^{2}}}} \tag{12}\]  
where \(M\) and \(N\) are the width and height of image \(f_{A}\) and \(f_{B}\). PSNR is used to measure the quality of the reconstructed image. If the PSNR value is larger, the quality of the reconstructed image is higher.  
\[RMSE=\sqrt{\frac{1}{M\times N}{\sum_{i=1}^{M}{\sum_{j=1}^{N}{\left(f_{\text{A }}-f_{\text{B}}\right)}^{2}}}} \tag{13}\]  
RMSE is used to evaluate the quality of the reconstructed image and to measure the deviation between the reconstructed image and the reference image. A small RMSE value indicates that the difference between the two images is small.
## 5 Experimental Analysis
### Experimental Setup  
This study simulates the management environment of in situ data within the InS system and unifies certain descriptions across all algorithms. The simulation experiments are conducted in Anaconda3 + PyCharm3.7.  
To ensure the stability and reliability of the experimental results, each algorithm was independently executed \(N=10\) times under the simulation parameters shown in Table 3 (each run includes data from \(max\_epochs=5000\) rounds). The reward values and various metrics recorded in each round are reported as the average over all experimental results.  
The adjustment factor \(\zeta=1.0\) indicates that the system is operating under a normal workload, while \(\zeta>1.0\) simulates higher load scenarios by increasing the expected access demand per round. The discount factor \(\gamma\) determines the extent to which future rewards influence current decisions, with higher values leading the agent to prioritize long-term returns. Epsilon \(\epsilon\) governs the balance between exploration and exploitation; it is initially set to a high value to encourage greater random exploration by the agent. Epsilon Decay \(\epsilon_{\text{decay}}\) is the decay factor for the exploration rate, gradually reducing \(\epsilon\) over the course  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Description** & **Algorithm** & **Code** \\  & & **Complexity** & **Complexity** \\ \hline \multirow{4}{*}{Baseline} & Without considering the delay and congestion of the system’s personalized demand queues, a queue is randomly selected for response in each round. & \(O(n)\) & \(O(n)\) \\ \hline \multirow{4}{*}{ABC} & The Artificial Bee Colony (ABC) optimization algorithm allocates and optimizes resources through exploration, following, and scouting processes, aiming to find the optimal media selection strategy. & \(O(n)\) & \(O(n)\) \\ \hline \multirow{4}{*}{DQN} & Based on the DQN algorithm, system states are considered for continuous decision-making optimization, selecting the best response queue in each round [29]. & \(O(n)\) & \(O(n)\) \\ \hline \multirow{4}{*}{DDQN} & A double Q-value update mechanism is introduced on the basis of the DQN algorithm, along with a target network, to address the issue of overestimation of action values in DQN [27]. & \(O(n)\) & \(O(n)\) \\ \hline \multirow{4}{*}{DDQN + PER (ours)} & Based on the DDQN algorithm, the introduction of the Prioritized Experience Replay mechanism enables more frequent training on experiences that have a greater impact on the Q-Network, thereby accelerating the learning process. & \(O(n)\) & \(O(n)\) \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-  
of training to shift the agent's behavior toward the learned policy and reduce reliance on random actions. Learning Rate \(\alpha\) defines the step size for updating the Q-network's weights to minimize the loss function; a lower learning rate contributes to a more stable training process.
# Design and Implementation of Virtual Security Function Based on Multiple Enclaves
## 3 Background
### Sgx
Intel SGX provides special instructions to enter and exit the enclave: EENTER and EEXIT. When an enclave exits, the processor will call a special internal routine called asynchronous exit (AEX), which retains the enclave's register states, clears the register, and sets the address of the error instruction to the value defined by EENTER. The ERESUME instruction is used to restore the state and allow the enclave to continue execution. SGX attestation. SGX offers two attestation mechanisms-local and remote. Local attestation is used between two enclaves running on the same platform. This paper uses local attestation to ensure that multiple enclaves run on the same platform and transmit information between multiple enclaves through a secure channel established by local attestation. Figure 1: Architecture description of Software Guard Extensions (SGX).
# Application of Three Metaheuristic Techniques in Simulation of Concrete Slump
## 3 Data Collection and Statistical Analysis  
The used database is created by measuring the slump of 103 concrete specimens based on a research by [PERSON] [2]. Seven slump influential factors, cement (C), slag (S), water (W), fly ash (F), superplasticizer (SP), fine aggregate (FA), and coarse aggregate (CA) are considered as input variables, while the slump is taken to be the output of the predictive models. According to reference paper, the standards of American society or testing and materials (ASTM) was considered for creating concrete specimens. Meanwhile, the conventional slump test (ASTM C143/C143M-00) [44] was used to determine the consistence of fresh concrete. More details about data provision and carried out tests are presented in References [2; 45].  
Considering the famous ratio of 80:20, the dataset was divided into the training (composed of 82 concrete slump tests) and testing (composed of 21 concrete slump tests) phases. The results of the statistical analyses (i.e., the values of minimum, maximum, mean, and standard deviation) of these factors are presented in Table 1. Moreover, Figure 4a-g show the graphical relationship between the slump and each effective factor.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Slump** & **Cement** & **Slag** & **Water** & **Fly ash** & **SP** & **FA** & **CA** \\  & **(cm)** & **(kg/m\({}^{3}\))** & **(kg/m\({}^{3}\))** & **(kg/m\({}^{3}\))** & **(kg/m\({}^{3}\))** & **(kg/m\({}^{3}\))** & **(kg/m\({}^{3}\))** & **(kg/m\({}^{3}\))** \\ \hline Minimum & 0.0 & 137.0 & 0.0 & 160.0 & 0.0 & 4.4 & 640.6 & 708.0 \\ Maximum & 29.0 & 374.0 & 260.0 & 240.0 & 193.0 & 19.0 & 902.0 & 1049.9 \\ Mean & 18.0 & 229.9 & 149.0 & 197.2 & 78.0 & 8.5 & 739.6 & 884.0 \\ Standard deviation & 8.7 & 78.9 & 85.4 & 20.2 & 60.5 & 2.8 & 63.3 & 88.4 \\ \hline \end{tabular}
\end{table}
Table 1: Statistical analyses of the used dataset.  
Figure 4: _Cont_.