esa-sceva/satcom-chunk-collection huggingface dataset:
# The Bivariate Empirical Mode Decomposition and Its Contribution to Grinding Chatter Detection
## 3 Application of a Feature Vector Extraction Method for Simulating Chatter Signals
### Chatter Feature Vector Extraction Based on BEMD and Hilbert Transform  
By BEMD decomposition and extraction of true IMFs, the chatter signal is decomposed into a series of true IMFs and a residue. According to Equations (15) and (16), the real-time variance and instantaneous energy of each true IMF can be calculated and then superposed, respectively, to construct synthetic chatter feature vectors for chatter detection and identification. For the sake of comparison and analysis, the chatter feature vectors should be normalized to keep their value between 0 and 1 [33]. The superposed and normalized real-time variance and instantaneous energy of IMFs are shown in Figure 9.  
In order to achieve good recognition in the above figure, the maximum and mean values of real-time variance and instantaneous energy in various grinding states can be determined by removing the interference of endpoints (Figure 10).  
Figure 10: (**a**) Maximum and mean values of real-time variance; (**b**) maximum and mean values of instantaneous energy.  
Figure 9: (**a**) The simulated chatter signal; (**b**) superposed and normalized real-time variance and instantaneous energy.  
From Figures 9 and 10, it can be clearly seen that real-time variance and instantaneous energy in various grinding states exhibit different behaviors. The amplitude of these feature vectors is almost steady in the stable grinding stage, and the maximum and mean values are both very close. While the amplitude drastically increases once the signal enters the transition stage, the mean value of two feature vectors increases 1.4 and 2.5 times respectively, and the maximum increases 1.8 and 3.7 times. Moreover, when the signal is in a chatter stage, the instantaneous energy fluctuates within a certain range that the mean value and maximum are close. However, as real-time variance increases continuously with time, the mean and maximum increase 1.9 and 1.63 times when compared to the transition stage. Though it is difficult to distinguish the transition and chatter states exactly, it is feasible to clearly determine the onset of grinding chatter. Therefore, these two feature vectors can be applied as early chatter detectors.
# Inactivation of Contaminated Fungi in Rice Grains by Dielectric Heating
## 2 Materials and Methods
### Experimental Desing for Dielectric Heating System
#### 2.3.4 Effect of Dielectric Heating on Fungal Growth  
The treated seventy-five rice samples in a sterile glass Petri dish (Figure 4a) were treated at different electric field intensities (150, 190, 225, 300, and 450 kV/m) and temperatures (70, 80, 90, 100, and 110 \({}^{\circ}\)C) using dielectric heating system. The non-treated rice sample was used as control with three replications. The rice samples were kept in a sterile glass Petri dish during the heating process using aseptic techniques [45]. After that 20 grains of treated and non-treated (control) rice samples were randomly selected and placed on a sterile PDA using aseptic techniques under a laminar airflow cabinet ([PERSON]/Mars 1500, Labogene Aps, Lynge, Denmark) (Figure 4b). Then, the plates were incubated at room temperature for five days. The fungal mycelium was observed using a stereo microscope (Stemi 305, Carl Zeiss, Oberkochen, Germany) and compared with control groups that were not exposed to the electric field. Fungal colonies on rice seeds were counted and fungal inactivation was calculated according to Equation (5):  
\[\text{fungal inactivation}\left(\%\right)=\left(1-\frac{N_{P}}{N_{T}}\right) \times 100\% \tag{5}\]  
where \(N_{P}\) is the number of positive rice grain, and \(N_{T}\) is the number of total rice grain.  
Figure 4: Samples for investigating fungal inactivation: (**a**) rice sample; (**b**) rice grains on PDA before incubation.
###### Abstract
During the LOCA condition, a large amount of hot steam is generated in the NCS, which leads to a simultaneous increase in temperature and pressure inside the NCS [17]. Therefore, the temperature effect under LOCA conditions is also a major concern in the analysis and evaluation of the NCS. However, previous investigations of liner corrosion have primarily concentrated on the internal pressure condition. The influence of the combined effects of liner corrosion and thermal pressure coupling loading on the leak-tight function of the NCS has not been thoroughly investigated. Last but not least, the NCS typically involves various uncertainties during construction and operation, and thus the safety assessment of the NCS under accident conditions should fulfill both deterministic and probabilistic performance objectives [18]. The probabilistic safety assessment (PSA) methodology is widely applied to assess the leakage risk of nuclear power plants under accident or disaster scenarios, and it provides an effective way to specify the probabilistic safety performance of the NCS [19; 20]. However, few studies have been carried out to assess the safety performance of the NCS under liner corrosion conditions within the PSA framework. With the background mentioned above, this paper evaluates the fragility and leakage risk of the NCS under LOCA conditions, considering different liner corrosion conditions. Firstly, the corrosion mechanism of containment liners is elucidated, and a refined simulation method is developed to investigate the localized corrosion effects of the liner. Utilizing the probabilistic FE method, a fragility assessment of the NCS subjected to thermal pressure coupling loads is carried out, and the median pressure capacity and the high-confidence-low-probability capacity (HCLPF) of the NCS under liner corrosion conditions are evaluated. Finally, the leakage risk of the NCS at different degrees of liner corrosion is discussed in detail. The findings in this paper can inform the probabilistic safety assessment (PSA) of the NCS under liner corrosion conditions.
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 3 Detailed Description of the Proposed Localization Scheme
### Determining the Projection of a Node on the Trajectory  
In this section, to determine the projection of a sensor node on the trajectory, a three-step method is designed, as shown in Figure 3. First, to model the nonlinear relationship between the arbitrary point location and the RSSI value on the trajectory, we perform SVR training on the discrete original location and the measured RSSI value to obtain a regression function. Then, to get more points and RSSI values, we extend the original location vector and the measured RSSI vector. An extended location vector is obtained by uniformly inserting new points in the original location vector, and the extended location vector is input to the regression function to predict the extended RSSI value vector. Finally, to select the point with the largest RSSI value, we compare the elements in the extended RSSI value vector. The point corresponding to the largest RSSI value is determined as the projection of the node on the trajectory.
# Enhancing Online Learning Through Multi-Agent Debates for CS University Students
## 4 Results
### Data Analysis
Similarly, representative quotes from student responses to open-ended questions for each group during the post-test on the topic of \"regularization\" are presented as follows:  
_Response using the \"video only\" (English translation): Although L2 regularization is more commonly used in deep learning, its widespread application does not render L1 regularization obsolete. L1 regularization offers unique advantages in feature selection  
Figure 9: Word cloud analysis of open-ended question responses provided by the students for the course titled “Regularization”. (**a**) Responses in the pre-test under the video-only testing condition. (**b**) Responses in the post-test under the video-only testing condition. (**c**) Responses in the pre-test under the video-based Q&A testing condition. (**d**) Responses in the post-test under the video-based Q&A testing condition. (**e**) Responses in the pre-test under the video-based Q&A+MAD testing condition. (**f**) Responses in the post-test under the video-based Q&A+MAD testing condition. and generating sparse models, making it highly effective in specific scenarios. Therefore, depending on the requirements of a particular task, both L1 and L2 regularization have their irreplaceable roles. Response using the \"video-based Q&A\" (English translation): In machine learning and deep learning, L1 and L2 regularization are two commonly used techniques to prevent model overfitting. L1 regularization penalizes large weights by adding the absolute values of the weights to the loss function, while L2 regularization achieves this by adding the squared values of the weights. Response using the \"video-based Q&A + MAD\" (English translation): Preventing overfitting is one of the primary objectives of regularization. While both L1 and L2 regularization can mitigate overfitting, they achieve this through different mechanisms. L1 regularization reduces the number of non-zero weights, whereas L2 regularization minimizes the magnitude of the weights. This diversity allows for the selection of the most suitable regularization method based on the specific problem and dataset. L1 regularization offers an alternative strategy for combating overfitting, complementing L2 regularization rather than opposing it. These responses illustrate that the \"video-only\" approach enables students to acquire basic and accurate knowledge, the \"video-based Q&A\" approach allows for more precise answers, and the \"video-based Q&A + MAD\" approach further enhances the breadth of student responses. In other words, there is an increasing depth and breadth of understanding as the learning condition becomes more interactive. These findings are also consistent with the research results of previous papers. Through AI-assisted learning technologies, high-quality personalized learning can be achieved, improving students' abstract thinking and critical thinking [42].
# Thermophysical Property Measurements with the Finite Bar
## 4 Results
### Results in the Small Fourier-Number Limit  
In the small Fourier-Number limit, the temperature increases at first near the heating cartridge and then diffuses to the adiabatic end of the bar. The temperature measurements for the small Fourier-Number can be extracted by the corresponding criteria: The beginning is defined by the temperature rise in the sensor at the heated end of the bar (\(x=0\) W) and the end of the phase is defined by a starting temperature rise in the diabatic end of the bar (\(x=L\)).  
For selected times of small Fourier-Numbers (\(Fo=0.022\), 0.035, 0.046, 0.059, 0.071, 0.083, 0.096) the temperature measurements at the sensor locations are given in Figure 4. The temperature measurements can be fitted to Equation (7) to determine a value for the thermal diffusivity \(a\). Fitted curves are shown by dashed lines. For the sample Material the curve fitting resulted in a value of \(a=3.8\cdot 10^{-5}\) m\({}^{2}\)/s for the thermal diffusivity. This is in the range of the values found in different data-sheets (Table 1).  
The temperature \(T(x=0,t)\) rise measured at the origin is shown in Figure 5. By fitting the constants, it can be clearly seen that the measurements correspond to the analytical curve as described by Equation (5). In this case, the fit leads to a value of \(\lambda\rho c_{p}=7.2\cdot 10^{8}\) kg\({}^{2}\) m/K\({}^{2}\)s\({}^{5}\). This value differs from the literature value (\(3.5\cdot 10^{8}\) kg\({}^{2}\) m/K\({}^{2}\)s\({}^{5}\)) [12] due to the uncertainty of the fit that depends critically on the exact start of the heating time. Knowledge of the specific heat flux \(\dot{q}_{0}\) allows determining the product \(\lambda\rho c_{p}\). Even if the density \(\rho\) is known, it is necessary to measure the specific heat capacity \(c_{p}\) before the conductivity \(\lambda\) can be computed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & \(\epsilon_{p}\) & \(a\cdot 10^{-5}\) & \(\lambda\) \\  & in **J/kgK** & in **m\({}^{2}\)/s** & **in W/mK** \\ \hline
1 & 1321.5 & 4.507 & 160.82 \\
2 & 1319.9 & 4.513 & \\
3 & 1319.1 & 4.515 & \\
4 & 1313.7 & 4.534 & \\
5 & 1317.2 & 4.522 & \\
6 & 1321.5 & 4.507 & \\
7 & 1335 & 4.461 & \\ \hline Mean & 1321.1 & 4.51 & 160.82 \\ \hline Datasheet [12] & 862 & 5.38–6.63 & 130–160 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results for the thermophysical properties obtained in the large Fourier-Number limit.  
Figure 4: Measured temperatures at different times at the sensor locations are given by (+), the fitted curve to the temperature (Equation (4)) is given by the dashed curve (–).
# A Survey of Game Theoretic Approaches to Modelling Decision-Making in Information Warfare Scenarios
## 3 Game Theory  
[PERSON] [21] writes that information warfare requires at least two types of players. These players include an offensive player who attacks an information resource and a defensive player who protects the information resource. Such players may be associated with individuals, organisations or nations. Offensive players include insiders, hackers, criminals, corporations, governments or terrorists. The goal of offensive information warfare operations for the attacker is to increase the availability or integrity of information, while for the defender the goal is to decrease the availability or integrity of information. This has the effect of positive payoff to the attacker and negative payoff to the defender. From this viewpoint, information warfare can be modelled as a game that is played between offensive and defensive players who select strategies with different payoff. Representing information warfare in a game-theoretic manner suggests that information warfare operations can be analysed with game-theoretical methods [2; 3; 7].  
Game theory [1] is a mathematical language for describing multi-person decision-making scenarios. The scenario is described as a game. Decision-makers are called players and their moves are called actions. Players receive payoff based on their own move and the moves of other players.  
A key advantage of this is the ability to examine a large numbers of possible threat scenarios that potentially occur in the cyber space [38; 39]. Another advantage of game theory is its ability to control future attacks by providing methods for suggesting probable actions with the predicted outcomes [3]. Computers can analyse combinations of parameters to consider large numbers of possible outcomes.  
We also acknowledge that there are challenges associated with the application of game theoretic analysis. [PERSON] et al. [38], for example, identify seven challenges in applying game theory to the domain of information warfare, including a limited database of relevant games, and the fact that the real-world 'game' may change, meaning that the set of legal moves may change. Despite these challenges, game theory offers us a well defined framework in which to analyse decision-making.  
The following sections provide further details defining different types of games, players, actions and payoff.
## 2 Study Area and Method
### Model Parameters and Input Data
#### 2.3.7 Nitrogen Deposition Data  
The nitrogen deposition data came from global gridded estimates of atmospheric deposition of total inorganic nitrogen (N), NH\({}_{x}\) (NH\({}_{3}\) and NH\({}_{4}\)\({}^{+}\)), and NO\({}_{y}\) (all oxidized forms of nitrogen other than N\({}_{2}\)O), and the data set was generated using a global three-dimensional chemistry transport (TM3) model with a spatial resolution of 5 degrees longitude by 3.75 degrees latitude [51; 52]. Based on this data, linear interpolation was used to interpolate the annual nitrogen deposition spatial distribution data of Zhejiang Province from 1985 to 2015.
## 5 Methodology
### Metrics
#### 5.3.1 Bit Error Rate  
For evaluating the optical camera communication link, we employed the bit error rate (BER) metric, computed as the total number of incorrect received-demodulated bits of the total number of bits transmitted. When \(N\) bits are transmitted, the minimum BER achievable is  
\[BER_{\text{min}}\leq\frac{1}{N}. \tag{5}\]
# MHBase: A Distributed Real-Time Query Scheme for Meteorological Data Based on HBase
## 2 Related work
### Distributed Architecture
#### 2.1.1 Horizontal Partitioning  
As shown in Figure 2, the table is divided into _HRegions_ (region for short, the smallest unit of distributed storage in HBase) that are stored in different _HRegionservers_ (also called _Regionserver_ (_RS_)). Rowkey is used to address all columns in one single row and it is sorted on the basis of Log-Structured Merge Tree (LSM-Tree) [14] to speed up searching. A region will become larger with an increment in the volume of data and it will be split into two equal daughter regions when a particular threshold is reached. One _RegionServer_ is composed of several regions but one region cannot be placed on multiple _RegionServers_. That is to say, a read and a write of one data entry will always be on the same _RegionServer_.
# HORSIC+: An Efficient Post-Quantum Few-Time Signature Scheme
## Appendix A Proof of Theorem 1
```
1:Input: Parameters \(n\), \(t\), \(k\), \(z\), \(w\), one-way challenge \(y_{c}\), and second preimage resistance challenge \(x_{c}\)
2:A value \(x\) that is either a preimage of \(y_{c}\) or a second preimage for \(x_{c}\) under \(f_{x}\) or \"fail\"
3:Generate HORSIC+ key pair : \((X,Y)=K_{\beta HORSIC}()\)
4:Choose indices \(\alpha\xleftarrow{\$}\{1,\dots,t\}\) and \(\beta\xleftarrow{\$}\{1,\dots,w\}\) uniformly at random
5:if\(\beta=w\)then
6: Set \(\vec{r}^{\prime}=\vec{r}\)
7:else
8: Choose index \(\gamma\xleftarrow{\$}\{\beta+1,\dots,w\}\) uniformly at random
9: Obtain \(\vec{r}^{\prime}\) by setting \(r^{\prime}_{i}=r_{i}\) for all \(i\in[1,w]-\{\gamma\}\) and \(r^{\prime}_{\gamma}=c^{\gamma-\beta-1}(y_{c},\vec{r}_{\beta+1,w})\oplus x_{c}\)
10: Obtain \(Y^{\prime}\) by setting \(y^{\prime}_{0}=(\kappa,\vec{r}^{\prime})\), \(y^{\prime}_{i}=c^{w}(x_{i},\vec{r}^{\prime})\) for all \(i\in[1,t]-\{\alpha\}\), and \(y^{\prime}_{\alpha}=c^{w-\beta}(y_{c},\vec{r}^{\prime}_{\beta+1,w})\)
11:Run \(\mathcal{A}^{Sign(X,\cdot)}(Y^{\prime})\)
12:if\(\mathcal{A}^{Sign(X,\cdot)}(Y^{\prime})\) queries \(Sign\) with message \(M\)then
13: Compute \((i_{1},i_{2},\dots,i_{k})\), \((a_{1},a_{2},\dots,a_{k})\), and \(ctr\) which corresponds to \(M\)
14:if there exist \(j\in\{1,2,\dots,k\}\) such that \(i_{j}=\alpha\)and \(w-a_{j}<\beta\)then
15:return \"fail\"
16: Generate signature \(\sigma\) of \(M\):  a. Run \(\sigma=(ctr,sig_{1},sig_{2},\dots,sig_{k})\gets Sign_{HORSIC+}(X,M,\kappa, \vec{r^{\prime}})\)  b. if there exists \(j\in\{1,2,\dots,k\}\) such that \(i_{j}=\alpha\)then \(sig_{j}=c^{w-a_{j}-\beta}(y_{c},\vec{r}_{\beta+1,w})\)
17: Reply to the query string \(\sigma\)
18:if\(\mathcal{A}^{Sign(X,\cdot)}(Y^{\prime})\) returns valid \((M^{\prime},\sigma^{\prime})\)then
19: Compute \((i^{\prime}_{1},i^{\prime}_{2},\dots,i^{\prime}_{k})\), \((a^{\prime}_{1},a^{\prime}_{2},\dots,a^{\prime}_{k})\), and \(ctr^{\prime}\) which corresponds to \(M^{\prime}\)
20:if\((sig^{\prime}_{1},sig^{\prime}_{2},\dots,sig^{\prime}_{k})\) is a permutation of \((sig_{1},sig_{2},\dots,sig_{k})\)then
21:return \"fail\"
22:else if there exists \(\mathbf{no}\ j\in\{1,2,\dots,k\}\) such that \(i^{\prime}_{j}=\alpha\)or \(w-a^{\prime}_{j}\geq\beta\)then
23:return \"fail\"
24:else if\(\beta=w\)then
25:return preimage \(c^{\gamma-w+a^{\prime}_{j}-1}(sig^{\prime}_{j^{\prime}},\vec{r}_{w-a^{\prime}_ {j}+1,w})\oplus r^{\prime}_{w}\)
26:else if\(c^{\beta-w+a^{\prime}_{j}-1}(sig^{\prime}_{j^{\prime}},\vec{r}_{w-a^{\prime}_ {j}+1,w})=y_{c}\)then
27:return preimage \(c^{\beta-w+a^{\prime}_{j}-1}(sig^{\prime}_{j^{\prime}},\vec{r}_{w-a^{\prime}_ {j}+1,w})\oplus r^{\prime}_{\gamma}\)
28:return \"fail\"
```  
**Algorithm A1**\(\mathcal{M}^{A}\)
# Tibetan Judicial Event Extraction Based on Deep Word Representation and Hybrid Neural Networks
## 3 Methodology  
### Overview  
This research presents a Tibetan judicial event extraction model (TJEE) that leverages deep word representations and hybrid neural networks. The architecture of the proposed TJEE model is illustrated in Figure 2, and it is composed of three main components:  
1. Deep word representation: For the Tibetan judicial text input sequence, the Tibetan syllables are separated as the basic unit, and the CINO model is utilized to generate a deep semantic representation vector containing contextual information. This vector is fed into the hybrid neural network, which provides rich dynamic contextual features.
2. Feature modeling: The overall semantics of the text is comprehensively modeled through hybrid neural networks. Specifically, BiLSTM captures long-distance dependencies by learning the contextual semantics embedded in the text; at the same time, convolution and pooling operations are performed on the Tibetan syllable vectors to extract the local features, thereby enhancing the focus on the key information; finally,the information from different feature levels is fused to form a unified semantic representation.
3. Event prediction: Based on the fused deep word representation, contextual features, and local features, CRF decoding is used to calculate the optimal sequence label distribution and finally complete the prediction task for event trigger words and associated arguments.
# On the Virtual Cell Transmission in Ultra Dense Networks
## 3 Transmission Schemes Based on Virtual Cell Merging
### Virtual Cell Merging with ZF and MVC-MMSE Transmission
#### 3.1.3 ZF Transmission  
We consider using ZF to eliminate intra-cluster interference, where only intra-cluster CSI is required. For each merged virtual cell cluster \(\mathcal{B}_{m}\) and the user group \(\mathcal{K}_{m}\) it serves, the downlink channel matrix can be denoted as \(\mathbf{H}_{\mathcal{K}_{m},\mathcal{B}_{m}}\in\mathbb{C}^{|\mathcal{K}_{m}| \times|\mathcal{B}_{m}|}\). Then, the ZF precoding matrix \(\mathbf{Z}_{\mathcal{K}_{m},\mathcal{B}_{m}}\) can be calculated as the pseudo-inverse of \(\mathbf{H}_{\mathcal{K}_{m},\mathcal{B}_{m}}\), i.e., \(\mathbf{Z}_{\mathcal{K}_{m},\mathcal{B}_{m}}=\mathbf{H}_{\mathcal{K}_{m}, \mathcal{B}_{m}}^{H}\left(\mathbf{H}_{\mathcal{K}_{m},\mathcal{B}_{m}}\mathbf{ H}_{\mathcal{K}_{m},\mathcal{B}_{m}}^{H}\right)^{-1}\). Nevertheless, the existence of \(\mathbf{Z}_{\mathcal{K}_{m},\mathcal{B}_{m}}\) requires \(\mathbf{H}_{\mathcal{K}_{m},\mathcal{B}_{m}}\) to have full row rank, where \(|\mathcal{B}_{m}|\leq|\mathcal{K}_{m}|\) must be ensured. Neither the virtual cell grouping method based on cell overlapping nor the virtual cell merging scheme based on interference graph can guarantee such a full row rank condition. As a result, MRT is still adopted in this circumstance, and the corresponding precoding matrix is denoted as \(\mathbf{R}_{\mathcal{K}_{m},\mathcal{B}_{m}}=\mathbf{H}_{\mathcal{K}_{m}, \mathcal{B}_{m}}^{H}\).  
Figure 2: Illustration of the interference graph-based virtual cell merging. Circles denote the initial cells; numbers on the edges denote the potential interference strength metric. Different colors stand for the result of virtual cell merging, where cells with the same color belong to the same merged virtual cell cluster.  
Considering the constraint that each user's precoding vector has unit norm, the precoding vector for user \(k\in\mathcal{K}_{m}\) under the ZF transmission scheme can be expressed as:  
\[\mathbf{w}_{k,\mathcal{B}_{m}}^{\text{ZF}}=\begin{cases}\mathbf{z}_{k,\mathcal{B }_{m}}/\|\mathbf{z}_{k,\mathcal{B}_{m}}\|&\text{if }|\mathcal{B}_{m}|\leq|\mathcal{K}_{m}|\\ \mathbf{r}_{k,\mathcal{B}_{m}}/\|\mathbf{r}_{k,\mathcal{B}_{m}}\|&\text{ otherwise}\end{cases} \tag{9}\]  
where \(\mathbf{z}_{k,\mathcal{B}_{m}}\) and \(\mathbf{r}_{k,\mathcal{B}_{m}}\) are the column vectors of \(\mathbf{Z}_{\mathcal{K}_{m},\mathcal{B}_{m}}\) and \(\mathbf{R}_{\mathcal{K}_{m},\mathcal{B}_{m}}\) corresponding to user \(k\) in \(\mathcal{K}_{m}\), respectively.