esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Work  
The model can be divided into three parts: the feature extraction module, attention mechanism control module and the recognizer model. The audio is converted into an image after the feature extraction module, and the image is processed by the CDCN so as to avoid information loss; then, it is transferred to the attention module to continuously pay attention to the contextual information, and finally it is transmitted to the classifier module to classify the audio, as shown in Figure 1.  
The causal convolutional marker for initialization is a way to provide the initial transcription of training audio in order to start iterative training. Although these recognizers do indicate the use of transcribed audio, they can be considered a legacy resource. If the acoustic field of the legacy system is similar to the audio at hand, it can be very useful. The mismatch of the sound range will reduce the effectiveness of this recognizer. However, this is a fairly simple model, as each class has only one Gaussian value, and these methods can improve performance on the basis of the current Transformer initialization.  
The encoder for traditional speech tasks uses the RNN to capture the temporal characteristics of speech and output higher-level features from the original input spectral features. Additionally, the latest architecture of the AED is the self-attentive structure proposed by Transformer of Neural Machine Translation (NMT), which learns the dependence of temporal information through temporal attention to the input features themselves, thus replacing the traditional RNN network layer. The key idea behind it is to generate a weighted average calculated by hidden units, using the information provided by the topic recognition system to dynamically obtain, round by round, the weights between the different components, which will depend on certain values obtained by the system, such as distance metric, similarity to the topic, etc. The interactions between the input signals depend on the signals themselves and are not predetermined by their relative positions as in the case of convolution. This allows the attention mechanism to capture remote interactions without increasing the number of parameters. In this way, we also evaluate the automatic clustering strategies, which are applied to the documents in the training dataset. The purpose is to create automatic topic clusters to verify whether these clusters can improve topic-based LMs compared with manually assigned topic tags of training datasets.
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 2 Overview of WSNs  
A WSN is an ad hoc network consisting of a set of sensor nodes, randomly fixed or dispersed in a given geographical area, communicating via a wireless link to autonomously collect, process, and transmit data in their environment to a special node, considered the collection point, called a sink [16; 22; 30; 31; 32]. The geographical area where the sensor nodes operate is called the field or area of interest. In a WSN, sensor nodes are able to self-organize to collect information about the environment where they are deployed. The transmission of the collected data can be done periodically or event-based, depending on the nature of the implemented application. The sink is a node with two or more network interfaces that act as a bridge between the WSN and the end-user's network (for example, a local area network or the Internet). The user can send requests to other nodes in the network via the sink, for example, to specify the type of data to be collected. For illustration purposes, Figure 1 shows the basic architecture of a WSN. The sensor nodes randomly deployed in a zone of interest and the sink located at the end of the zone are responsible for recovering the data collected by the sensors. Most often, the sink collects and processes network data, thus sending only relevant information to the user. It can also receive commands to run on the internal network from it (the user). The collected data are processed and analyzed by the user.
## 3 Derivation of the Lower Capacity Bounds for UWOC with APD
In this second case, the average and peak power constraints (11) and (12) are still satisfied. However, due to that \(\alpha\) tends to \(1/3\) for \(\mu\downarrow 0\), the input distribution \(Q(\cdot)\) for this second \(\alpha\) case is reduced to the following expression by approaching \(\mu\) in (26) to zero [15] \[Q(x)\triangleq\left\{ \begin{array}{ll}\frac{1}{\sqrt{4P_{x}}},&0<x\leq P\\ 0,&else\end{array} \right..\] (32) Similarly, under this new distribution, we have \[h(x)=logP-1+log2,\] (33) and \[\frac{1}{2}E_{Q}[log\Big{(}1+H\zeta^{2}x\Big{)}]=\frac{1}{2}log\Big{(}1+H \zeta^{2}P\Big{)}-1+\frac{1}{\sqrt{H\zeta^{2}P}}tan^{-1}\sqrt{H\zeta^{2}P}.\] (34)  
By rewriting (25), that is,  
\[C\geq\ h(x)+f(\epsilon,H)-\frac{1}{2}E_{Q}\Big{[}log2\pi e\sigma^{2}\Big{(}1+ H\zeta^{2}x\Big{)}\Big{]}\]  
\[=h(x)+f(\epsilon,H)-\frac{1}{2}log2\pi e\sigma^{2}-\frac{1}{2}E_{Q}[log\Big{(} 1+H\zeta^{2}x\Big{)}], \tag{35}\]  
the lower capacity bound is  
\[C_{Low}=log2P-1-\frac{1}{2}log2\pi e\sigma^{2}+f(H)\, \tag{36}\]  
where  
\[f(H)=\frac{1}{2}log(H)+\frac{1}{2}log\frac{\epsilon H+2\sigma^{2}\zeta^{2}}{ \epsilon}-\frac{\epsilon H}{\sigma^{2}\zeta^{2}}+\frac{\sqrt{\epsilon H( \epsilon H+2\sigma^{2}\zeta^{2})}}{\sigma^{2}\zeta^{2}}\]  
\[-\frac{1}{2}log\Big{(}1+H\zeta^{2}P\Big{)}-\frac{1}{\sqrt{H\zeta^{2}P}}tan^{ -1}\sqrt{H\zeta^{2}P}. \tag{37}\]
## 4 Anomaly Detection
### Joint Probability of the Observed Sequence  
The threshold \(\rho(z)\) is associated with the joint probability of the observed sequence. Firstly, in order to derive the forward-backward procedure of first-order auto-regressive HMM for the joint state sequence \(z_{1:T}\) given observation \(y_{1:T}\), plus the initial observation \(y_{0},\ (T\geq 1)\), we have this problem when dealing with conventional HMM. Here the auto-regressive structure of AR-HMM makes this situation more complicated; a modified forward-backward method could solve the problem efficiently. Let us first write the joint distribution with the Markov property \(p(y_{t}|y_{t-1},\ldots,y_{1})=p(y_{t}|y_{t-1})\) that is  
\[p(y_{0:T}) =\sum_{t=1}^{T}p(y_{t}|y_{1:t-1}), \tag{17}\] \[p(y_{t}|y_{1:t-1}) =\sum_{k=1}^{K}p(z_{t}=k|y_{1:t-1})p(y_{t}|z_{t}).\]where \(\theta_{z_{t}}\) represents the observation parameters for the trained HMM and the \(z_{1:T}\) is a state path over the hidden-state space. As such, if the state path is estimated, the maximum probability of the observation sequence can be obtained. However, since the true state path is hidden from the observations, for computational convenience, the general approach would be to use the maximum likelihood state at any given moment, but this would neglect uncertainty. Instead, we use a marginal probabilistic representation over hidden states at each time step (see details in Section 4.3). Thus, the log-probability at time \(t\) is derived by computing the logarithm of the sum of exponentials over all hidden states  
\[\mathcal{L}_{t}=log\ \sum_{k=1}^{K}\exp(\frac{\alpha_{t}(k)\cdot\beta_{t}(k)}{p (y_{0:T})}), \tag{18}\]  
where \(\alpha_{t}(k)=p(z_{t}=k|y_{0:t})\), \(\beta_{t}(k)=p(y_{t+1:T}|z_{t})\) are the forward message passing and backward message in the standard forward-backward algorithm, respectively.  
\[\begin{split}\alpha_{t}(k)&=\frac{1}{p(y_{t}|y_{0: t-1})}p(y_{t}|z_{t}=k)p(z_{t}=k|y_{1:t-1}),\\ p(z_{t}=k|y_{0:t-1})&=\sum_{j}p(z_{t}|z_{t-1}=j)p(z _{t-1}|y_{0:t-1}).\end{split} \tag{19}\]  
The denominator represents the probability of a sequence observations, which can be calculated by Equation (17). Assume that we recorded \(N\) sequential data for each skill and the length of sequence \(n\in N\) is \(T_{n}\). Thus, we can get a set of log-probabilities vector of each skill  
\[\mathcal{L}^{1:N}=\{\mathcal{L}^{1}_{T_{1}},\mathcal{L}^{2}_{T_{2}},\ldots, \mathcal{L}^{N}_{T_{N}}\}. \tag{20}\]  
The above implies that the forward-backward procedure can be used to find the most likely state for any moment \(t\). It cannot, however, be used to find the most likely sequence of states.
# A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas
## 3 5G Security Architecture: A Systematic Approach
### Radio Access Network  
#### 3.2.1 Non-Public Networks  
Non-Public Networks (NPNs), which are sometimes also referred to as private networks or public-private networks, are instances of an architecture that allows operators to partition their network infrastructure for the use of an exclusive party. Access to the network can be limited to a special network slice that has specific QoS, access controls, authentication requirements, and more. A Non-Public Network can also be solely owned by the exclusive party to achieve even higher data integrity and privacy. An article published by 3 GPP [48] shares that in some instances, an organization may want to house and control all data passed through a network and restrict everything to within the bounds of the organization. In this specific case, the operators of the network achieve heightened security as they are in control of the architecture and its users and employ their own hardware for the Core and RAN. This provides three main advantages: optimized coverage, lower latency, and reduced network uncertainty by optimizing confidentiality, integrity, and availability. 3 GPP outlines more information on NPNs in TS 22.261 [49]. The authors in [50; 51] review the outlined security measures for NPNs along with potential use cases and considerations. Use cases for NPNs are also discussed in [52], which include emergency critical communications, smart city video surveillance, and services with Time-Sensitive Networking (TSN).  
Each use case in this paper provides a unique infrastructure challenge, which helps to further define the challenges of building a robust private 5G network for multiple use cases. For emergency communications, for example, the network is proposed to serve as a \"pop-up\" network, which would require a set coverage area of the private 5G network. The smart city surveillance use case requires significant computing power at the edge, as streaming the video content from all surveillance cameras is impractical. In this case, micro-cells for higher-quality coverage and computer vision for emergency identification at the edge of the network would be required. TSN services, including autonomous mobile robots and factory process automation, were also outlined, focusing on the need for clear communication in a service area without re-cabling every device.
## 3 Results and Discussion
### Quantification of the Four Marker Compounds by LC-MS/MS  
In Table S3, hyperoside, querctrin, and ginseng Rg1 were detected in the negative ion mode ([M\(-\)H]-) at \(m\)/z 463.2, 447.0, and 799.2, respectively, while 6-gingerol was detected in the positive ion mode ([M+H]+) at \(m\)/z 295.3. The precursor ion (Q1) and product ion (Q3) for the LC-MS/MS MRM analysis were set as shown in Table S3 [30, 31, 32, 33]. Various parameters such as linear range, regression equation, \(r^{2}\), LOD, and LOQ for simultaneous determination using the LC-MS/MS MRM method are presented in Table S6. For the quality evaluation of DGJT, quantification of the 4 marker components was performed using the LC-MS/MS MRM method. These markers, hyperoside, querctrin, ginseng Rg1, and 6-gingerol, were detected at 1.63, 2.03, 2.71, and 6.33 min, respectively (Figure 2, Figure S4 and Table S6). The concentrations of 4 marker compounds, hyperoside, querctrin, ginseng Rg1, and 6-gingerol, were 1.07, 0.27, 2.42, and 0.86 mg/g, respectively.
## 3 Results
### Heat-Treated Amaranth Grain
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factor** & **AMEn, kcal/kg** & **Metabolisability, \%** & **Dry Matter Digestibility** \\  & & & **(DMD), \%** \\ \hline \multicolumn{4}{l}{Heat-treated amaranth grain} \\ (HTAG), g/kg & & & \\ \hline
0 & 3060  & 67.11 [FOOTNOTE:]Footnote : footnotemark: [ENDFOOTNOTE] \\
150 & 3136  & 65.04 [FOOTNOTE:]Footnote : footnotemark: [ENDFOOTNOTE] \\
300 & 3251  & 59.57 [FOOTNOTE:]Footnote : footnotemark: [ENDFOOTNOTE] \\
450 & 3556  & 63.28 [FOOTNOTE:]Footnote : footnotemark: [ENDFOOTNOTE] \\
600 & 3788  & 62.69  \\ \(p\)-value & 0.0001 & 0.0001 & 0.0001 \\ \hline \multicolumn{4}{l}{Enzyme (\({}^{\mathrm{E}}\)), g/kg} \\ \hline
0 (\({}^{-\mathrm{E}}\)) & 3329 & 76.11 & 62.36 \\
0.55 (\({}^{+\mathrm{E}}\)) & 3397 & 78.35 & 64.72 \\ SEM & 17.62 & 0.24 & 0.42 \\ \(p\)-value & 0.002 & 0.0001 & 0.011 \\ \hline \multicolumn{4}{l}{HTAG \(\times\) Enzyme} \\ \hline
0 \(\times\) 0 & 3021  & 64.62 [FOOTNOTE:]Footnote : footnotemark: [ENDFOOTNOTE] \\
150 \(\times\) 0 & 3107  & 65.59 [FOOTNOTE:]Footnote : footnotemark: [ENDFOOTNOTE] \\
300 \(\times\) 0 & 3236  & 63.54 [FOOTNOTE:]Footnote : footnotemark: [ENDFOOTNOTE] \\
450 \(\times\) 0 & 3514  & 63.54 [FOOTNOTE:]Footnote : footnotemark: [ENDFOOTNOTE] \\
600 \(\times\) 0 & 3720  & 62.56 [FOOTNOTE:]Footnote : footnotemark: [ENDFOOTNOTE] \\
0 \(\times\) 0.55 & 3098  & 69.60 [FOOTNOTE:]Footnote : footnotemark: [ENDFOOTNOTE] \\
150 \(\times\) 0.55 & 3166  & 64.49 [FOOTNOTE:]Footnote : footnotemark: [ENDFOOTNOTE] \\
300 \(\times\) 0.55 & 3266  & 63.65 [FOOTNOTE:]Footnote : footnotemark: [ENDFOOTNOTE] \\
450 \(\times\) 0.55 & 3597  & 63.03 [FOOTNOTE:]Footnote : footnotemark: [ENDFOOTNOTE] \\
600 \(\times\) 0.55 & 3855  & 62.83 [FOOTNOTE:]Footnote : footnotemark: [ENDFOOTNOTE] \\ SEM & 39.40 & 0.55 & 0.94 \\ \(p\)-value & 0.032 & 0.004 & 0.007 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: AMEn, metabolisability and dry matter digestibility (DMD) values of broiler chickens fed different levels of heat-treated amaranth grain (HTAG) with (\({}^{\mathrm{E}}\)) and without (\({}^{-\mathrm{E}}\)) enzyme from 35 to 42 d of age in the first experiment.
## 1 Introduction
Spectral kurtosis and autoregressive model are the most commonly applied techniques for this purpose [14]. These are addressed in the context of diagnosis process using the MATLAB scripts. Simulation datasets are prepared to explain the characteristics of the raw signal and the process of how to retrieve the fault information out of it. The techniques are then applied to the real datasets made by the authors to demonstrate the practical applications. The datasets by the Case Western Reserve University, which have been a standard reference in testing the diagnostic algorithms, are also used to demonstrate the general nature of the techniques. The paper is organized as follows. In Section 2, the basic theory and procedures in the bearing diagnostics are introduced. Section 3 addresses how to implement the bearing diagnosis using the simulation data. Practical diagnoses using the MATLAB codes are also performed with the real datasets in Sections 4 and 5. Lastly, conclusions are presented in Section 6.
# Evaluation of Clumping Effects on the Estimation of Global Terrestrial Evapotranspiration
## 2 Modeling Methodology
### ET Modeling  
The Penman-Monteith equation was used in BEPS to calculate ET (in m s\({}^{-1}\)) [14; 31]:  
\[ET=\left[\frac{\Delta(R_{n}-G)+\rho_{a}c_{p}(e_{s}-e_{a})g_{a}}{\Delta+\gamma \left(1+\frac{g_{a}}{g_{s}}\right)}\right]/\lambda_{v} \tag{1}\]  
where \(R_{n}\) represents net radiation (W m\({}^{-2}\)) by the surface of interest and \(G\) represents conductive heat flux (W m\({}^{-2}\)) away from the surface. \(\rm{e_{s}}\) is the saturated water vapor pressure (kPa) at the surface. \(e_{a}\) represents the actual water vapor pressure (kPa) above the surface. \(g_{a}\) represents the aerodynamic conductance (m s\({}^{-1}\)) from evaporating surface to a reference height. \(\gamma\) represents the psychometric constant (\(\approx\)0.066 kPa \({}^{\circ}\)C\({}^{-1}\)). \(\rm{A_{v}}\) represents the latent heat of vaporization. \(\rho_{a}\) is the air density. \(\rm{c_{p}}\) represents the specific heat of the air. \(\rm{g_{s}}\) represents the surface conductance to water vapor (m s\({}^{-1}\)). \(\Delta\) represents the change of saturated water vapor pressure with air temperature and it is calculated as [40]:  
\[\Delta=\frac{2503.16e^{\frac{17.27T_{a}}{1+27.3}}}{\left(T_{a}+237.3\right)^{ 2}} \tag{2}\]  
where Ta represents air temperature (\({}^{\circ}\)C). In BEPS, \(ET\) is the sum of nine components and it is calculated as:  
\[ET=T_{o}+T_{u}+E_{o}+E_{u}+S_{o}+S_{u}+E_{s}+E_{g}+S_{g} \tag{3}\]  
where \(T_{o}\) and \(T_{u}\) are transpiration rates of overstory and understory dry canopies, respectively. \(E_{o}\) and \(E_{u}\) are evaporation from intercepted liquid water in overstory and understory canopies. \(S_{o}\) and \(S_{u}\) is sublimation rates of intercepted snow by overstory and understory canopies, respectively. \(E_{s}\) is soil evaporation rate. \(E_{g}\) is the evaporation rate of ponded water on the ground and \(S_{g}\) is the sublimation rate of the snow on the ground.
# Enhanced Gradient-Based Local Feature Descriptors by Saliency Map for Egocentric Action Recognition
## 3 Saliency Map-Based Local Feature Descriptors for Egocentric Action Recognition
### Saliency Map-Informed Bin Response Generation
```
Input :\(\mathcal{F}r_{i}\): the \(i\)th 2D video frame, \(\mathcal{F}r_{i}\in\mathbb{R}^{m\times n}\) Output :\(S\): saliency membership of the \(i\)th frame Procedure\(getSaliencyMap(\mathcal{F}r_{i})\)
1: Construct the RGB video frames: \(\mathcal{F}r_{i}\rightarrow\mathcal{F}_{i}^{R}\), \(\mathcal{F}_{i}^{G}\), \(\mathcal{F}_{i}^{B}\)
2: Convert \(\mathcal{F}_{i}\) in the RGB frames to LAB frames: \(\hat{\mathcal{F}}_{i}=Rgb2 Lab(\mathcal{F}_{i})\);
3: Reconstruct the frame \(\hat{\mathcal{F}}_{i}\) by \(\hat{\mathcal{F}}_{i}=D[sgn(D(\hat{\mathcal{F}}_{i}))]^{-1}\);
4: Calculate the LAB channel average \(\hat{\mathcal{F}}_{i}\) using \(\hat{\mathcal{F}}_{i}=\hat{\mathcal{F}}_{i}^{L}+\hat{\mathcal{F}}_{i}^{A}+ \hat{\mathcal{F}}_{i}^{B}/3\);
5: Smooth the 2D single channel \(\hat{\mathcal{F}}_{i}\) by \(\hat{\mathcal{F}}_{i}=\kappa_{g}*(\hat{\mathcal{F}}_{i}\circ\hat{\mathcal{F}} _{i})\);
6: Compute \(S\) by normalizing the \(\hat{\mathcal{F}}_{i}\), \(0<\mu_{Attractiveness}^{i}(p)\leq 1,\forall p\in\mathcal{F}r_{i}\);
7:return\(S\);
```  
**Algorithm 1**Saliency Membership Calculation Procedure. The extra computational complexity of the proposed approach, compared with the original versions, mainly lies in the calculation of the saliency map, as the saliency information is integrated into the proposed approach by a simple multiplication operation. The computational cost of the saliency map approach used in this work is generally moderate, as it is only a fraction of the cost of other saliency algorithms [15].
## 4 Discussion and Conclusions  
In the rapidly expanding world of the Internet of Things, network attacks are rising. Therefore, enhancing the performance of intrusion detection systems is of paramount importance to safeguard IoT applications and connected devices. Many researchers are devoted to developing secure, lightweight IDS frameworks using machine learning techniques. When ensuring the quality of machine learning-based IDS, two critical factors are data feature handling and model generalization.  
Research findings indicate that data preprocessing techniques like data cleaning, One-hot encoding, and normalization can enhance dataset availability. Through feature selection and oversampling in feature engineering, the proportion of minority class samples can be increased while reducing data dimensionality and computational overhead, thereby improving the algorithm model's effective representation of data and accurate classification. Moreover, introducing the Optuna framework for hyperparameter optimization of the CatBoost algorithm further enhances its accuracy, leading to superior classification results. Looking towards the future, we can focus on the following key aspects:  
1. Utilizing random undersampling: Consider employing random undersampling to reduce the number of majority class instances and optimize dataset balance. This reduces the demand for computational resources and maintains dataset representativeness.
2. Lightweight model framework: Strive to reduce model complexity and computational burden to enhance efficiency and real-time performance. Exploring simplified model structures, feature selection algorithms, or model compression techniques can achieve lightweight objectives.
3. Real-time environment testing: Besides evaluating model performance in the laboratory, conducting comprehensive tests in real-time environments will provide more realistic scenarios and data to further validate the model's reliability and practicality.  
By conducting further research and improvements in these areas, we can elevate the model's performance and make it more suitable for real-world network intrusion detection scenarios. These endeavors will strengthen the security and reliability of the IoT environment, effectively countering the ever-growing network threats. Our goal is to protect IoT platforms, enabling them to monitor traffic data in real time and respond promptly to intrusion behavior, ensuring the safety and stable operation of the Internet of Things.  
Conceptualization, Z.W.; methodology, Y.Z.; writing--original draft preparation, Z.W.; supervision, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The experimental data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.