esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Experimental Results
#### 3.1.2 Optical Emission Spectroscopy
In this work, the emission spectrum of the OH \(A^{2}\Sigma\to X^{2}\Pi\) band was used for the determination of the rotational, vibrational, and excitation temperatures in the AC plasma reactor. Figure 7a shows an example of this spectrum in the AC plasma reactor at the applied AC voltage of 22 kV. Rotational, vibrational, and excitation temperatures were  
Figure 5: Emission spectrum of the AC-PPP at the AC voltage of 22 kV and the position z = 5 mm. Figure 6: Relative intensities for emission lines of various species as a function of position at an AC voltage of 22 kV and 1 cm distance between electrodes. calculated using SPECAIR software that fits a simulated spectrum to experimental data to estimate these temperatures (see Figure 7a) [26]. For this simulation work, all the factors affecting the line shape, such as the instrumental resolution or the collisional broadenings, were considered. By applying this fitting to vibro-rotational bands, the rotational, vibrational, and excitation temperatures were obtained in terms of position with an error of 7% (Figure 7b). For the three temperatures, their values were constant along the reactor, due to the parameters oscillating between the electrodes during the cycle of AC voltage (see next section). These results correspond to time average values during this cycle. Figure 7b shows that the experimental rotational temperature was about 2000 K for all positions. The vibrational and excitation temperatures were about 5000 K and 18,000 K, respectively, which means the plasma was in 2-T conditions, where the electron temperature was higher than the gas temperature. The energy of the heavy particles and electrons were enough to produce the conversion of the CO\({}_{2}\) molecules.
## 4 Analysis of the Tunnel Construction Simulation Results
### Deformation of Arch Bottom  
During the tunnel excavation, a deformation will occur around the entire tunnel. Table 3 shows the maximum deformation values of all the surveying points. The maximum deformation of the vault, the vault bottom, and the spandrel are recorded. The maximum deformation is not the vault deformation. The deformation of the bottom of the tunnel is larger. The deformation diagram of the bottom of the arch with different excavation methods is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Excavation Method/Surveying Point Location/Maximum Deformation (mm)** & **Surveying Point at 0 m** & **Surveying Point at 20 m** & **Surveying Point at 40 m** & **Surveying Point at 60 m** \\ \hline B1 & \(-\)12.0 & \(-\)11.7 & \(-\)11.7 & \(-\)12.2 \\ C1 & 12.6 & 11.8 & 11.6 & 12.0 \\ B2 & \(-\)11.9 & \(-\)11.5 & \  
It can be seen from Table 3 that the deformation at the top and bottom of the tunnel is much larger than that around the tunnel. During the excavation of the CRD method, the maximum uplift value reached 12.6 mm, which appeared at the 0 m surveying point C1, and this was not the final deformation. After a certain construction time, the deformation value rebounded, and the maximum rebound value was 0.6 mm. During the whole CRD excavation, the deformation curve detected at 0 m is similar to the inverse curve, but as it progresses, the deformation value rebounds and finally stabilizes at 12.0 mm. The main reason for this behavior is that in the excavation process, the in situ stress is broken and reorganized, which will have a certain impact on the surrounding rock. In addition, although the tunnel is supported, the stress of the surrounding rock will cause the tunnel to be damaged over time, and therefore a slow deformation occurs. The maximum uplift value in the CD method excavation reached 13.9 mm, which is also consistent with the CRD method excavation process. The maximum uplift value is not the final deformation value, and the maximum rebound value is 0.6 mm. During the excavation of the full-section method, the maximum uplift value reached 14.1 mm, and the rebound also occurred with a maximum value of 0.5 mm. The largest arch bottom deformation occurs in the full-section excavation, which also has the highest deformation speed, and the maximum uplift value at the 20 m surveying points C1 and C2 is 14.1 mm.  
By analyzing Figures 6 and 7, it can be deduced that in vault settlement, horizontal convergence of the tunnel circumference, and deformation of the tunnel bottom, CRD is the most decent excavation method. The tunnel deformation rate of the CD method is not much different from that of the full-section method. The full-section method has the largest deformation around the tunnel and the largest deformation rate; therefore, CRD excavation is the most decent for large-section tunnel excavation in tunnel settlement and deformation.  
Figure 7: Deformation diagram of the arch bottom with different excavation methods.
## 5 Analysis of the Dynamic Response Law of Non-Homogeneous Slopes
### Dynamic Response Analysis of Non-Homogeneous Slopes
#### 5.2.4 Dynamic Stability Analysis of Non-Homogeneous Slopes Under Blasting Loads  
Figure 21 illustrates the variation of safety coefficients over time for slopes with different shape parameters. It is evident from the graph that the safety coefficient of slopes with shape parameters is generally greater than that of slopes without shape parameters. This indicates that slopes with higher heterogeneity are more unstable, and the safety coefficient generally exhibits a trend of first decreasing and then increasing with time. Overall, the dynamic safety coefficients of slopes with various shape parameters are all greater than 1, indicating that under the action of blasting vibration waves, none of the slopes exhibit a trend towards instability. Comparing slopes with different joint dip angles under the same blasting vibration loads, it is evident that the instability tendency of structural slopes is greater than that of heterogeneous slopes, which is related to the stronger dynamic amplification effect of structural slopes.  
Figure 20: \(\mathrm{M_{PGA}}\) variation pattern of slope surface with different shape parameters.  
Figure 22 depicts the trend of the safety coefficient reduction rate with respect to shape parameter variation. From the graph, it is evident that as the shape parameter increases, the reduction rate of the slope safety coefficient decreases. This indicates that more homogeneous rock masses are less susceptible to the influence of blasting loads, with the maximum reduction rate reaching only 4.6%. Furthermore, the overall reduction rate of the safety coefficient is much smaller compared to joint dip angle slopes.
## 5 User Interface and Set Up
### Test Case: Voting Process
#### 5.2.1. Voter Registration and Pre-Election Preparations  
* **Database Preparation**: The Central Election Commission (CEC) compiles a voter list and uploads it to the MongoDB database. The administrator ensures the accuracy of voter data and manages the voter registration process (see Figures 12-14).  
* **Administrator's Role in Voter Registration**: The administrator is responsible for validating voter credentials and managing the OTP verification process via Twilio. They ensure that each voter is correctly registered and able to participate in the election securely.
# Development of Hybrid Airlift-Jet Pump with Its Performance Analysis
###### Abstract  
A new pump, called the hybrid airlift-jet pump, is developed by reinforcing the advantages and minimizing the demerits of airlift and jet pumps. First, a basic design of the hybrid airlift-jet pump is schematically presented. Subsequently, its performance characteristics are numerically investigated by varying the operating conditions of the airlift and jet parts in the hybrid pump. The compressible unsteady Reynolds-averaged Navier-Stokes equations, combined with the homogeneous mixture model for multiphase flow, are used as the governing equations for the two-phase flow in the hybrid pump. The pressure-based methods combined with the Pressure-Implicit with Splitting of Operators (PISO) algorithm are used as the computational fluid dynamics techniques. The validity of the present numerical methods is confirmed by comparing the predicted mass flow rate with the measured ones. In total, 18 simulation cases that are designed to represent the various operating conditions of the hybrid pump are investigated: eight of these cases belong to the operating conditions of only the jet part with different air and water inlet boundary conditions, and the remaining ten cases belong to the operating conditions of both the airlift and jet parts with different air and water inlet boundary conditions. The mass flow rate and the efficiency are compared for each case. For further investigation into the detailed flow characteristics, the pressure and velocity distributions of the mixture in a primary pipe are compared. Furthermore, a periodic fluctuation of the water flow in the mass flow rate is found and analyzed. Our results show that the performance of the jet or airlift pump can be enhanced by combining the operating principles of two pumps into the hybrid airlift-jet pump, newly proposed in the present study.  
jet pump; airlift pump; hybrid pump; two-phase flow +
Footnote †: journal: Applied Physics Letters B
## 5 Conclusions  
This paper proposes coACO to improve the performance of the basic ACO in solving the BN structure-learning problem. The coACO divides the entire ant colony into several small ant groups and uses the pheromone as shared information and the cooperative factor. The cooperative evolution process of ant groups via collaborative interaction, which includes communicating pheromone information and sharing optimal networks between groups, can greatly improve the efficiency and accuracy. The coevolution mechanism based on DE is performed to lead the cooperative evolution process for all ant groups. DE operators can effectively adjust the cooperation information and lead all ant groups to evolve toward the optimum in a cooperative manner. Different from the widely used methodologies that combine ACO with constraint-based techniques, our work mainly focuses on improving the inherent search capability of ACO. Our algorithm employs the BIC metric as the scoring function; however, it is also applicable to other score metrics, such as K2 and MDL. Comparison test results show that our proposed coACO algorithm outperforms the ACO, K2, and B algorithms in terms of quality of the reconstructed BNs. Due to the coevolution characteristic, the coACO can adjust the pheromone distribution to the best state in just a few iterations, and thus yield the optimal network structure much faster. In a word, our developed coACO-based BN structure-learning algorithm is effective, accurate, efficient, and easy to implement. Our future work will apply the coACO to a more complex BN structure-learning problem in a real project with incomplete data. Conceptualization, X.Z. ; Funding acquisition, S.J. ; Project administration, S.J. ; Software, X.L. ; Writing--original draft, Y.X. ; Writing--review & editing, X.Z. This work is supported by the Beijing Municipal Education Commission, the National Natural Science Foundation of China (No. 61703012), and the Beijing Natural Science Foundation (No. 4182010).
# Motion Target Localization Method for Step Vibration Signals Based on Deep Learning
## 3 Description of Our Method
### Feature Extraction
#### 3.2.3 Time Sequence Feature Extraction Module  
During pedestrian movement, footsteps are typically smooth and continuous, with subtle changes in walking states between adjacent moments and continuous speed variations. Additionally, the gait pattern exhibits periodicity, with footstep vibration signals showing periodic rises and falls. However, a single convolutional network cannot capture the complete feature representation of footstep vibration signals. Considering that the convolution operation in the local feature extraction module compresses the feature matrix \(\overline{\mathbf{U}}^{r}\) along the time axis, while there still exists temporal correlation within \(\mathbf{D}_{8}^{r}\), this paper employs Bi-LSTM for cascading to further extract temporal features of the signal while reducing computational complexity.  
To fully grasp the periodic nature of the signal, the local spatiotemporal feature \(\mathbf{D}_{8}^{r}\) is used as input to Bi-LSTM, segmented into \(T\) time steps. By utilizing the gating units of LSTM, the forget gate filters out noise, retaining the motion state from the previous time step and passing it to the next time step. The bidirectional structure of Bi-LSTM separately computes the hidden layer information from the future \(\overrightarrow{\mathbf{h}}_{t_{j}}^{r}\) and from the past \(\overleftarrow{\mathbf{h}}_{t_{j}}^{r}\), then concatenates them to generate the output \(\mathbf{D}_{9}^{r}\). The specific computation process is as follows:  
\[\overrightarrow{\mathbf{h}}_{t_{j}}^{r}=LSTM(\overrightarrow{\mathbf{h}}_{t _{j-1}}^{r},\mathbf{D}_{8}^{r}(t_{j}),\overleftarrow{\mathbf{c}}_{t_{j-1}}^ {r})t_{j}\in[1,T] \tag{7}\]  
\[\overleftarrow{\mathbf{h}}_{t_{j}}^{r}=LSTM(\overleftarrow{\mathbf{h}
# Curvature Sensing and Control of Soft Continuum Robots Using e-Textile Sensors
## 3 Deep-CNN Shape Sensing
### Training of CNN Model  
During the training phase of our convolutional neural network (CNN) models, the Mean-Squared Error (MSE) loss function is utilized. This function is integral to the deep regressor model that estimates the shape parameters of the continuum robot. The MSE loss function quantifies the discrepancy between the CNN model's predicted output, denoted as \(\hat{\mathbf{y}}(\zeta)\), and the actual reference output \(\mathbf{y}\), which corresponds to the robot's curvature. This loss function is particularly suitable for our regression task because it accurately measures the errors in continuous output values. The MSE is mathematically expressed as:  
\[\text{MSE}=\frac{1}{N}\sum_{i=0}^{N-1}[\hat{\mathbf{y}}(\zeta)^{i}-\mathbf{y}^{i}]^{2} \tag{1}\]  
In the presented equation, the symbol \(\zeta\) denotes the network parameters, encompassing the learnable weights and biases. To enhance the model's performance, we employ the Adam optimizer [29], starting with an initial learning rate of \(10^{-3}\). Furthermore, fine-tuning the hyper-parameters is essential for particular numerical examples. For instance, we set the training batch size to 32 and run the model for 500 epochs.
## 1 Introduction
A key characteristic of ABAC is that it allows the specification and enforcement of various types of constraints, such as the separation-of-duty (SOD) constraints, cardinality constraints, binding-of-duty (BOD) constraints, and user-capability (UC) constraints, which are irrelevant to the access control mechanism implemented in the system and can reflect the different security requirements of organizations and ensure the security of ABAC systems [12]. As a significant security policy discussed in this paper, the SOD constraint prevents an individual from performing all the steps involved in an important task, as a single user is more likely to abuse his or her privileges, while multiple users can supervise with each other while performing a task. Typically, a \(k\)-\(n\) SOD constraint requires that at least \(k\) users complete a special task that requires \(n\)-many operating permissions [13] and has been widely used in the banking industry and in military systems. However, none of the conventional policy-engineering methods consider the SOD constraint. Moreover, although mutually exclusive authorization rules (MEAR) constraints have been used to enforce a given separation of authorization rules (SOAR) constraint in an existing ABAC system [13], a SOAR constraint takes the form of authorization rules as its input, while an SOD constraint takes the form of access tuples. Therefore, it is necessary to convert the access tuples into authorization rules to facilitate the enforcement of the SOD constraints, which is also an interesting issue. To address the above issues, this paper proposes a novel method called policy-engineering optimization with visual representation and separation-of-duty constraints (PEO_VR&SOD). In summary, the main contributions of this work are as follows:  
1. To reduce the mining scale and enhance the interpretability of policy mining, we use the visual technique with Hamming distance to rearrange, portray, and partition an original authorization matrix and discover a minimal set of authorization rules from rearranged submatrices. We present a policy mining algorithm and compare its performance to the existing methods. 2. To verify whether SOD constraints can be satisfied in a constructed policy engineering system, we convert the SOD constraints into SOAR constraints using the method of SAT-basedmodel counting. We construct MEAR constraints from the SOAR constraints to implicitly enforce the given SOD constraints and evaluate the performance of the PEO_VR&SOD. The rest of the paper is organized as follows. We discuss the related work in Section 2 and present some preliminaries that are discussed in the following sections in Section 3. Section 4 proposes a novel method for policy engineering optimization, which involves two aspects: (1) policy mining with visual representation and (2) policy optimization with separation of duty constraints. We present experimental evaluations and compare their performance with existing studies in Section 5. Section 6 concludes the paper and discusses future work.
# A Fast Calibration Method for Sensors of Atmospheric Detection System
## 4 Evaluation
### Controller Parameter Setting and Evaluation  
The PID parameters of the system are designed using the PID Tuner toolbox in Simulink, which is commonly used in the PID parameter tuning process, to confirm the initial optimal PID parameters of \(P=1.986\), \(I=0.069\), \(D=5.302\); then, we set the filter order \(N=0.570\) and add the designed PID controller to the loop of the temperature control system. The controller parameters are adjusted according to the MAC PID controller design method proposed in Section 3.1. The minimum adjustment time \(t_{s}\geq 5\) s, temperature change limit \(T_{s}\leq 15\)\({}^{\circ}\)C, overshoot \(\sigma\%\leq 5\%\), and adjustment time \(t_{b}\leq 150\) s are set during the adjustment process. After adjustment, the PID parameters are \(P=1.030\), \(I=0.033\), and \(D=0\).  
At the same time, several mature and perfect PID controller design methods are selected for comparison [45; 46]. Using the Z-N PID parameter design method [38], a set of parameters is obtained as \(P=2.383\), \(I=0.367\), and \(D=3.874\). Without human intervention, the simulation results show that this set of parameters causes the system to diverge; so, it cannot be used. Using the TFB PID parameter adjustment method in [39] and the Fuzzy PID method of automatic parameter adjustment in [40], the corresponding system response curve is obtained. The details are described below.
# Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook
## 3 Methodology
### Feature Extraction  
Text Representation (TF-IDF, Word2 Vec, FastText)  
Feature extraction transforms textual data into numerical representations for computational analysis. The following techniques were used:  
* TF-IDF: This statistical method evaluates how important a word is by computing its frequency across documents.
* Word2 Vec: A neural network-based technique for word encodings that capture semantic similarities among words to provide more contextual understanding [36].
* FastText: An advanced word embedding method that considers subword information, making it ideal for handling Roman Urdu's non-standard spellings. FastText allows words with similar meanings to be represented closely in vector space, enhancing classification accuracy.
## 2 Literature Review  
In cooperative games, there are some classical solutions for profit allocation, such as Shapley value [9] and Banzhaf value [10]. However, these solutions are usually applied when the profit information of the players and the coalition is precisely and completely known. In fuzzy environments, these classical cooperative game solutions are often limited to effectively solve practical problems, and there is still a gap in the research methodology. In addition, the classical Shapley value mainly focuses on the marginal contribution of the players and ignores the dissatisfaction of the players. To address this issue, this paper aims to determine the least square contribution when the satisfaction of the player is maximized by introducing the least squares method. We plan to replace the marginal contribution in the classical Shapley value with this least square contribution and improve the Shapley value based on it. Finally, this paper will also use trapezoidal fuzzy numbers to extend the application of the improved Shapley value to ensure that the improved Shapley value can be applied in fuzzy environments and that the profit allocation results are both fair and reasonable and consistent with the axiomatic system of the classical Shapley value.  
For logistics alliances, the cooperative profit allocation scheme is crucial. A fair and reasonable distribution scheme can enhance the stability of the alliance; on the contrary, if the logistics enterprises are unable to obtain satisfactory profits from the cooperation, it may lead to the disintegration of the alliance [15]. In view of the fuzzy nature of the cost and profit of cooperation of some logistics enterprises, this paper studies in depth the solution of the cooperation game in a fuzzy environment. In order to explore the profit allocation solution of e-commerce logistics alliance of forest products under the fuzzy environment, this paper reviews the following literature topics: (1) cooperative game, (2) fuzzy cooperative game, (3) contribution excess, (4) least square method, and (5) e-commerce logistics of forest products. Among them, the cooperative game and fuzzy cooperative game literature form the basis of this paper's research. And the improved Shapley value with trapezoidal fuzzy numbers proposed in this paper is inspired by the contribution of the literature on excess and on the least square method. Finally, this paper reviews the literature on e-commerce logistics of forest products to provide a reference scheme of cooperative profit allocation for e-commerce logistics alliances of forest products. For the literature on cooperative games and fuzzy cooperative games, this paper summarizes the classical cooperative game solutions and their properties; the literature on contribution excess provides theoretical support for this paper's definition of the contribution excess of the players; the literature on the least square method elucidates a way to minimize the dissatisfaction of the players; and the literature on e-commerce logistics of forest products allows us to summarize some of the unique characteristics of e-commerce logistics of forest products.