esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Control Design and Stability Analysis
### Boundedness and Convergence Analysis
Substituting the auxiliary system (47a), (47b) and arranging, yields \(\dot{V}_{\psi}=-K_{\psi 1}\psi_{1}^{2}+\psi_{1}\psi_{2}-K_{\psi 2}\psi_{2}^{2}+ \psi_{2}W_{\psi}\). Factorizing, yields  
\[\dot{V}_{\psi}\leq-(K_{\psi 1}-1/2)\psi_{1}^{2}-\beta_{1}(K_{\psi 2}-1/2)\psi_{2}^{2 }+\frac{1}{4\beta_{2}(K_{\psi 2}-1/2)}W_{\psi}^{2}\]  
Arranging yields  
\[\dot{V}_{\psi}\leq-k_{o}V_{\psi}+\frac{1}{4\beta_{2}(K_{\psi 2}-1/2)}\sup_{0 \leq s\leq t}(W_{\psi}^{2})\]  
where \(k_{o}\) is a positive constant (75). Therefore,  
\[V_{\psi}\leq V_{\psi 0}e^{-k_{o}t}+\frac{1}{4k_{o}\beta_{2}(K_{\psi 2}-1/2)} \sup_{0\leq s\leq t}(W_{\psi}^{2})\]  
Using the definition of \(V_{\psi}\) (78), we get  
\[\Omega_{e}=\left\{e:|e|\leq C_{b}+\frac{1}{\sqrt{2k_{o}\beta_{2}(K _{\varphi 2}-1/2)}}\sqrt{\sup_{0\leq s\leq t}(W_{\psi}^{2})}\right\} \tag{74}\]0  
From this it follows that \(\psi_{1}\) converges asymptotically to \(\Omega_{\psi 1}\),  
\[\Omega_{e}=\left\{e:|e|\leq C_{b}+\frac{1}{\sqrt{2k_{o}\beta_{2}(K _{\varphi 2}-1/2)}}\sqrt{\sup_{0\leq s\leq t}(W_{\psi}^{2})}\right\} \tag{74}\]1  
From Equation (77), the above result and result Ti, it follows that \(e=x_{1}-y_{d}\) converges asymptotically to the compact set \(\Omega_{e}\) (74). This completes the proof of Tiv. The parameter updating errors are bounded despite input saturation, so that excessive increase of updated parameters is avoided. From result Tiv of Theorem 2, it can be observed that the bound of the steady tracking error can be made small by choosing large values of \(K_{\psi 1}\), \(K_{\psi 2}\).
# A Coordination Technique for Improving Scalability of Byzantine Fault-Tolerant Consensus
## 5 Conclusions  
This paper proposes a coordination technique for improving the scalability of BFT-based consensus algorithms. The technique is composed of four steps: (1) electing a prime node; (2) collecting transactions from transaction pools; (3) processing equal and unequal transactions; and (4) generating a new block. Our key idea is to control a conditional execution of the consensus algorithm by dividing the transaction pool into equal and unequal transactions and secondly dividing common and trouble transactions among unequal transactions. The consensus algorithm is then executed only for trouble transactions, and the results are merged and finalized through sharing the transactions throughout all blockchain nodes.  
Based on this approach, we carried out three experiments to respond to three research questions. As a result of the experiments, the use of PBFT equipped with our approach showed 4.75 times the performance improvement on average compared to using PBFT only. In addition, our approach contributed to improving the performance by a maximum of 61.81% of the performance, compared to the single-use of IBFT. In addition to this, we showed the correlation of performance and trouble transactions associated with the transaction issue interval and the number of blockchain nodes.  
Although our approach showed the scalability improvement of BFT-based consensus algorithms, it has explicit limitations. First, the consensus coordinator is centralized so that it exposes the coordinator to the single point failure issue. Second, our approach does not address the recovery issue of the coordinator when a system has failed or restarted. Third, our approach should be tested in the real-world environment where diverse synchronization issues exist such as clock synchronizations throughout distributed nodes. For future work, we plan to carry out more research on distributing the centralized consensus coordinator and establishing recovery strategy from the system failure in the real-world environment.  
Conceptualization, J.S. and D.K.; Funding acquisition, S.P.; Investigation, J.S.; Methodology, S.K.; Supervision, S.P.; Writing--original draft, J.S., D.K. and S.P.; and Writing--review and editing, S.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2017-0-01628) supervised by the IITP (Institute for Information & communications Technology Promotion).  
The authors declare no conflict of interest.
# Hybrid Representation of Sensor Data for the Classification of Driving Behaviour
## 4 Experimental Evaluation
### Datasets
#### 4.1.1 Uah  
The UAH dataset has been introduced by [PERSON] et al. [12] to facilitate benchmarking of computational approaches for driving behaviour analysis. It comprises of data acquired by means of mobile phone sensors and cameras. UAH route samples represent three types of driving behaviour: normal, aggressive, and drowsy, under various conditions: motorway or secondary road, with six drivers of different genres and ages (Table 2). As demonstrated by [PERSON] et al., drowsy driving behaviour is manifested with slow lane changes and can be effectively detected by means of a camera. Since, in this work, we limit our study in sensor data, we focus on distinguishing between normal and aggressive behaviour, using only the respectively labelled routes from UAH. Overall, in our experiments we use 23 UAH route samples encompassing acceleration measurements in the lateral and longitudinal axis of the vehicle, acquired at 10 Hz by means of the inertial sensor of an iPhone [12].
## 3 Basic Settings for Gamma/Hadron Separation
### Morphological and Stereoscopic Parameters  
The analysis of Cherenkov radiation events captured by the ASTRI Mini-Array demands a comprehensive parameterization framework that encapsulates both the spatial and temporal characteristics of the atmospheric showers. Each event reconstruction relies on multiple complementary parameters that collectively characterize the fundamental properties of the primary particle interaction and subsequent shower development.  
In our study, we have utilized the main morphological and stereoscopic parameters commonly adopted in Cherenkov analysis [3] and already included in the standard analysis procedure of A-SciSoft.  
Our analysis framework incorporates the following key parameters to characterize Cherenkov shower images:  
* **Shower Size Parameters**:
* \(\log_{10}(SIZE)\): Logarithmic measure of the total photoelectron content in the cleaned image;
* \(DENS\): Compactness measure defined as \(\log_{10}(SIZE/(WIDTH\times LENGTH))\);
* \(CONC\): Image concentration, computed as the ratio between the two brightest pixel intensities and total shower size.
* **Shape Parameters**:
* \(WIDTH\): Minor axis of the best-fit shower ellipse [3];
* \(LENGTH\): Major axis of the best-fit shower ellipse [3];
* _M3 LONG_: Third-order moment characterizing image elongation.
* **Image Structure Metrics**:
* _LEAKAGE_: Ratio of edge pixel signals to total shower size;
* _NUIMCORE_: Count of shower core pixels [14];
* _NUMBOUNDARY_: Count of shower boundary pixels [14];
* _NUMISLAND_: Number of isolated pixel clusters (used for filtering).
* **Stereoscopic Reconstruction Parameters**:
* _NUSUSDTEL_: Number of telescopes contributing to stereoscopic reconstruction;
* _TELIP_: Distance between reconstructed shower core and telescope position;
* _STMAXH_: Reconstructed height of shower maximum.  
To carry out our tests, we have added to this set a different subset of the temporal parameters described in Section 4.
## 4 Experiments and Analysis
### Datasets  
The Twitter datasets for our empirical analysis include Sentiment140 ([[http://help.sentiment140.com/for-students](http://help.sentiment140.com/for-students)]([http://help.sentiment140.com/for-students](http://help.sentiment140.com/for-students)) accessed on 20 November 2021), US-Airline ([[https://www.kaggle.com/crowdflower/twitter-airline-sentiment](https://www.kaggle.com/crowdflower/twitter-airline-sentiment)]([https://www.kaggle.com/crowdflower/twitter-airline-sentiment](https://www.kaggle.com/crowdflower/twitter-airline-sentiment)) accessed on 20 November 2021), Sentiment140-MV, Sentiment Dataset for Afghanistan (SDAA) ([[https://www.kaggle.com/kamyab20/sentiment-dataset-for-afghanistan-sd4a](https://www.kaggle.com/kamyab20/sentiment-dataset-for-afghanistan-sd4a)]([https://www.kaggle.com/kamyab20/sentiment-dataset-for-afghanistan-sd4a](https://www.kaggle.com/kamyab20/sentiment-dataset-for-afghanistan-sd4a)) accessed on 20 November 2021). The above datasets have been widely used in sentiment analysis tasks so that the experiment results have an accurate evaluation. The Sentiment140 dataset is scraped from Twitter by Sandford graduate students [51]. Each tweet is labeled positive and negative according to its emotional sentences, and there are 248,576 positives and 80,000 negatives. Currently, this dataset is one of the most used standard datasets for text classification [19, 34, 36, 37, 38, 40, 44]. The US-Airline dataset is available on the Kaggle website, which contains 14,641 tweets. This dataset was collected about the US airline problem in 2015 and divided into positive, negative, and neutral sentiment categories [27, 34, 41, 42, 43]. After removing neural tweets, we obtain 11,541 tweets for this research. Sentiment140-MV is the modification version (MV) of sentiment140 with 18,309 tweets. [PERSON] and [PERSON] [26] modified the sentiment140 dataset into 4000 tweets and 1500 tweets individually. We pick more tweets to see the effect of our model. SD4A is collected by [PERSON] et al. [52] about Afghanistan's security status from 29 March 2018 to 21 June 2018. It contains 18,309 positive and 18,539 negative tweets. Detailed statistics of the dataset are listed in Table 1.
## 2 Materials and Methods
### Proposed Model Architecture
#### 2.3.3 Sequential Head Models  
To process the sequence of segment embeddings generated by the base Sentence Transformer, a sequential head model is applied. It aims to capture dependencies and aggregate information across the segments of a document. We experimented with three different architectures for this head model: GRU, LSTM, and Transformer. All head models took the sequence of 512-dimensional embeddings as input and projected them into a final output embedding space of dimension 256. They utilized an attention mechanism (specifically Bahdanau attention) over the sequence of hidden states to produce the final aggregated representation before the last linear projection.  
The GRU head applies a Gated Recurrent Unit (GRU) layer (torch.nn.GRU) with a single layer (NUM_LAYERS = 1) and a hidden layer size of 256 (HEAD_HIDDEN_SIZE). The GRU processes the input sequence of embeddings. The sequence of output hidden states from the GRU layer is then passed through a Bahdanau attention mechanism to compute a context vector. This context vector is finally passed through a linear layer to produce the 256-dimensional output embedding.  
LSTM Head  
This head uses a Long Short-Term Memory (LSTM) layer (torch.nn.LSTM) configured similarly to the GRU head: a single layer with size of 256. It processes the sequence of embeddings, and its output hidden states are aggregated using the Bahdanau attention  
Figure 1: Workflow of the proposed job–candidate matching system.  
mechanism. A final linear layer maps the resulting context vector to the 256-dimensional output embedding.  
The Transformer head utilizes a Transformer encoder architecture. It first projects the 512-dimensional input embeddings to the model's hidden layer (256) using a linear layer. Then, a single-layer Transformer encoder (torch.nn.TransformerEncoder with a torch.nn.TransformerEncoderLayer) processes this sequence. The encoder layer uses multihead self-attention (e.g., 4 heads) and a feedforward network. The output sequence from the Transformer encoder is aggregated using the Bahdanau attention mechanism, followed by a final linear layer projecting to the 256-dimensional output embedding.
# Drilling Fluid and Cement Slurry Design for Naturally Fractured Reservoirs
## 5 Lost Circulation While Cementing through Naturally Fractured Reservoirs
### Cement Slurry and Set Cement Properties
#### 5.1.3 Set Cement Compressive Strength  
The set cement serves to protect and support the casing, as well as deliver the necessary zonal isolation for the life of the well. In general, as the density of the cement slurry decreases, so does the strength of the set cement. The cement slurry density needs to be  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Extender** & \begin{tabular}{c} **Minimum Achievable Density of** \\ **Cement Slurry (kg/m\({}^{3}\))** \\ \end{tabular} & **Common Concentration** \\ \hline \multicolumn{3}{c}{**Active Extenders**} \\ \hline Bentonite & 1380 & Up to 20\% BWOC \\ \hline Diatomaceous Earth & 1320 & Up to 40\% BWOC \\ \hline Silica & 1200 & Up to 28\% BWOC \\ \hline Fly Ashes & 1572 & Fly Ash/Cement ratio is 1:3 to 3:1 (+2\% bentonite) \\ \hline Sodium Silicates & 1332 & \
\begin{tabular}{c} Solid sodium silicate (Sodium \\ metasilicate): 0.2\% to 3.0\% BWOC \\ Liquid sodium silicate (water glass): 0.8 \\ to 2.31/sk \\ \end{tabular} \\ \hline \multicolumn{3}{c}{**Inert Extenders**} \\ \hline Expanded perlite & 1440 &
\begin{tabular}{c} Cement/Perlite ratio is (sk:kg) 1:14 to 1:56 \\ (+2 to 4\% bentonite BWOC) \\ \end{tabular} \\ \hline Gilsonite & 1440 & Up to 23 kg of gilsonite/sk of cement \\ \hline Powdered (ground) coal & 1430 & 5.8 to 11.3 kg of powdered coal/sk of cement (+up to 12\% bentonite BWOC) \\ \hline Microspheres (glass and ceramic) & 960 (840) & Up to 150\% BWOC \\ \hline Nitrogen & 720 & As required \\ \hline \hline \end{tabular}
\end{table}
Table 7: Some common cement extenders and minimum achievable slurry density range.  
light enough to avoid fracturing the formation, while retaining mechanical properties of a cement sheath to resiliently withstand the forces of fracture stimulation and production cycling while maintaining zonal isolation integrity throughout the life of the well. The required minimum compressive strength of set cement is approximately 3.5 MPa. The time required for a cement slurry to harden into a set cement of minimum compressive strength (\"wait-on-cement time\") depends on the water-to-cement ratio and the cement components and additives. As a rule, the addition of an extender reduces the compressive strength of cement [44].
# Landslide Displacement Prediction Based on Variational Mode Decomposition and GA-Elman Model
## 3 Research Area
### Landslide Monitoring Data Preprocessing  
This paper selected the No. 3 GNSS monitoring station, which recorded obvious deformation of the Shuizhuyuan landslide in the Three Gorges Reservoir area, for data analysis. We used as the research object the data produced at the observation point across a period of 840 days from 1 July 2018 to 18 October 2020. The data were processed in terms of weeks, and a total of 120 weeks of data were obtained (Figure 5). The data for the first 80 weeks were used as the training set used to train the model and determine its relevant parameters. The data from 81 to 100 weeks were used as the test set of the model to compare the results of each model and determine the optimal prediction model. The data from 101 to 120 weeks were used as the validation set of the model to compare the error between the prediction results of the optimal model and the actual displacement, in order to verify the feasibility of the model. To determine the impact of different combinations of training sets on the prediction accuracy of the model, and to analyze the timeliness of the displacement data, four groups of different training sets were constructed to train the model, as shown in Table 1. The test set took the data from the middle 20 weeks, i.e., the data from 81 to 100 weeks. The optimal training combination was determined by comparing the operation results of different combinations of training sets input into the GA-Elman model.
# Non-Classic Atmospheric Optical Turbulence: Review
## 2 Measurements and Synthesis
### Non-Classic Turbulence Emulators  
In the laboratory conditions, simulation of a non-classic air turbulence with the controllable anisotropy ratio and the non-Kolmogorov exponent can be achieved by mixing the hot and cold air streams flowing from the opposite directions with different speeds and passing through a fine mesh [48] (see Figure 5). The power law exponent and the anisotropy ratio can then be related to the beam wander analysis of a sufficiently narrow laser beam propagating through mixed air in a direction orthogonal to the air flows. The experimental campaigns indicated that, in fact, achieving the classic turbulence regime precisely is not an easy task, and it does not generally hold for equal temperatures and velocities of the two air streams.  
Figure 6 shows data for the Hurst exponents \(H\) in horizontal and vertical directions in the case of unequal air flow speeds with (top) hot air moving faster and (bottom) cold air moving faster. The Hurst exponent provides an alternative measure of self-similarity (longterm memory) of a random process, calculated directly from the time series. It is limited to interval \(0<H<1\) and characterizes positive/negative correlation for \(H<0.5/H>0.5\). The dashed line indicates isotropic turbulence with \(H=5/6\), which corresponds to the Kolmogorov case. It is evident that in the former case, \(H\) is more consistent in both directions and approaches the value of \(5/6\) for sufficiently high air speeds, while in the later case, the anisotropy is more pronounced but is independent from the air speed. The case of equal air speeds resembles the top subfigure but with more evident tendency to \(H=5/6\) at high air velocities (not shown).  
Figure 5: The schematic diagram of a turbulator used for non-classic turbulence simulations. Reprinted with permission from Reference [48].  
Figure 6: Data obtained with turbulator shown in Figure 5 for unequal hot and cold air flow speeds. Reprinted with permission from Reference [48].
# A Self-Scrutinized Backoff Mechanism for IEEE 802.11 ax in 5G Unlicensed Networks
## 4 Analytical Model  
We formulate the analytical evaluation of proposed COSB mechanism with saturation throughput and average delay, on the assumption of ideal channel conditions, i.e. no hidden terminal and capture effects. In the analysis, we assume the fixed number of WEs, each of which is always willing to transmit the data frame, i.e., the network is assumed as a saturated traffic environment. Initially, we study the behavior of a tagged WE with a discrete-time Markov chain model (DTMC) [8; 9], and we obtain the stationary transmission probability \(\gamma\) for the tagged WE. Since the proposed COSB does not reset the backoff stage to its initial value (that is to zero) after successful transmission, the transmission attempt for every new data frame remains recursive within the backoff stage state dimension. To accurately analyze the performance of COSB, we formulate a recursive discrete-time Markov chain model (R-DTMC). Later, by knowing the exact events that can occur on the communication channel within a randomly selected slot-time, we formulate the normalized throughput and average delay of the proposed COSB mechanism.
## 2 Mathematical and Physical Formulation
### Cascaded Lattice Boltzmann Method (CLBM)
#### 2.2.1 CLBM for the Flow Field
The continuous central moments of the Maxwell-Boltzmann distribution in continuous velocity space are equivalent to the equilibrium central moments of \(f_{i}^{eq}\)[24],  
\[\left|\widetilde{\Gamma}_{i}^{eq}\geq\left[\rho,0,0,\rho C_{s}^{2},0,0,0,\rho C _{s}^{4}\right]^{T}\right. \tag{14}\]  
of \(f_{i}\) resulting in the Cascaded collision operation. The equilibrium Distribution function \(f_{i}^{eq}\) is defined by the Maxwell-Boltzmann equilibrium distribution function by  
\[f_{i}^{eq}=\rho\omega_{i}(1+\frac{u.C_{i}}{C_{s}^{2}}+\frac{(u.C_{i})^{2}}{2C_ {s}^{4}}-\frac{u.u}{2C_{s}^{2}} \tag{15}\]where \(\rho\) is the fluid density, and \(C_{s}=\sqrt{\frac{1}{3}}\) is the sound speed on a lattice. A generalized local equilibrium is the analogous EDF [28; 29]. The forcing terms in discrete velocity space are expressed by [PERSON] et al. [35] as follows:  
\[R_{i}=\frac{\mathbf{F}}{\rho}\frac{(\mathbf{e_{i}}-\mathbf{u})}{C_{s}^{2}}f_{i}^{eq} \tag{16}\]  
In central-moment space, the forcing source terms are uniformly specified as [28; 29],  
\[|C_{i}>=NM|R_{i}>=\left[0,F_{x},F_{y},0,0,0,C_{s}^{2}F_{y},C_{s}^{2}F_{x},0\right] \tag{17}\]  
In the streaming step, the post-collision discrete DFs in space \(x\) stream to their neighbors \(x+e_{i}\Delta t\) along the characteristic lines as usual,  
\[f_{i}=(\mathbf{X}+\mathbf{e_{i}}\Delta t,t+\Delta t)=f_{i}^{*}(\mathbf{X},t), \tag{18}\]  
The discrete DFs after a collision are ascertained by \(\left|f_{i}^{*}>=M^{-1}N^{-1}\right|\widetilde{f_{i}^{*}}>.\) To get the Hydrodynamics variables, the following steps are performed:  
\[\rho=\sum_{i=0}^{8}f_{i}\,,\ \rho u=\sum_{i=0}^{8}f_{i}e_{i}+\frac{\Delta tF}{2} \tag{19}\]  
The current approach utilizes the incompressible approximation [36; 37], whereby, i.e., \(\rho=\rho_{0}+\delta\rho\) and \(\delta\rho\) are the density fluctuations. The incompressible Navier Stokes may be replicated by the Low-Mach number limit by using the Chapman-Enskog analysis [28]. The relaxation parameters are connected to the kinematic and bulk viscosities. by \(v=\left(\frac{1}{v_{*}}-0.5\right)C_{s}^{2}\Delta t\) and \(\varepsilon=\left(\frac{1}{v_{0}}-0.5\right)C_{s}^{2}\Delta t\), respectively.
###### Abstract  
The current research on concrete and cementitious materials focuses on finding sustainable solutions to address critical issues, such as increased carbon emissions, or corrosion attack associated with reinforced concrete structures. Geopolymer concrete is considered to be an eco-friendly alternative due to its superior properties in terms of reduced carbon emissions and durability. Similarly, the use of fibre-reinforced polymer (FRP) bars to address corrosion attack in steel-reinforced structures is also gaining momentum. This paper investigates the bond performance of a newly developed self-compacting geopolymer concrete (SCGC) reinforced with basalt FRP (BFRP) bars. This study examines the bond behaviour of BFRP-reinforced SCCG specimens with variables such as bar diameter (6 mm and 10 mm) and embedment lengths. The embedment lengths adopted are 5, 10, and 15 times the bar diameter (d\({}_{\text{b}}\)), and are denoted as 5 d\({}_{\text{b}}\), 10 d\({}_{\text{b}}\), and 15 d\({}_{\text{b}}\) throughout the study. A total of 21 specimens, inclusive of the variable parameters, are subjected to direct pull-out tests in order to assess the bond between the rebar and the concrete. The result is then compared with the SCGC reinforced with traditional steel bars, in accordance with the ACI 440.3R-04 and CAN/CSA-806-02 guidelines. A prediction model for bond strength has been proposed using artificial neural network (ANN) tools, which contributes to the new knowledge on the use of Basalt FRP bars as internal reinforcement in an ambient-cured self-compacting geopolymer concrete.  
self-compacting geopolymer concrete; basalt; fibre-reinforced polymer; pull-out test; bond strength prediction; ANN model