esa-sceva/satcom-chunk-collection huggingface dataset:
# Anomaly Detection for IOT Systems Using Active Learning
## 5 Results
### Comparative Analysis  
The research conducted in our study, titled \"Anomaly Detection for IoT Systems through the Application of Active Learning,\" surpasses previous investigations by achieving a remarkable accuracy rate of 99.75% when evaluated on the UNSW-NB15 dataset (Table 7). Our model's utilization of active learning facilitated the dynamic selection of highly informative data points for labeling. This approach effectively minimized the requirement for a large number of labeled data during the training process while maintaining a high accuracy level. This approach becomes particularly advantageous in situations where the acquisition of annotated data is expensive and time-consuming, but still yields enhancements in the model's performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Approach** & **Accuracy** & **Dataset** \\ \hline
[7] & SVM & 92.6\% & UNSW-NB15 dataset \\
[10] & SVM & 90.50\% & UNSW-NB15 dataset \\
[14] & VLSTM & 96.7\% & UNSW-NB15 dataset \\
[16] & RF & 96\% & UNSW-NB15 dataset \\
[22] & SVM & 94.3\% & UNSW-NB15 dataset \\
[24] & RF & 98.67\% & UNSW-NB15 dataset \\ Our approach & Active learning & 99.75\% & UNSW-NB15 dataset \\ \hline \hline \end{tabular}
\end{table}
Table 7: Recent work related to anomaly detection.  
This study employed the random forest classifier as the chosen model due to its proven effectiveness on the UNSW-NB15 dataset. The random forest classifier, an ensemble learning technique, uses many decision trees to enhance accuracy and mitigate the risk of overfitting. This approach can capture the intricate relationships between the various attributes and the desired outcome, resulting in a model that exhibits superior performance.  
To find anomalies in IoT systems, earlier research used supervised learning techniques like SVM, RF, and VLSTM on the UNSW-NB15 dataset. The accuracy scores of the investigations ranged from 90.50% to 98.67%.  
To optimize the performance of our model, our study employed a range of feature engineering techniques, including scaling and normalization of the dataset. Furthermore, in the context of active learning, our model used various query methods based on uncertainty, including least confidence, margin sampling, and entropy sampling. Utilizing several query techniques enabled our model to effectively examine data distribution and select the most informative examples for labeling. By employing feature engineering techniques, active learning methodologies, and a random forest classifier, a robust anomaly detection model for IoT devices was successfully developed, exhibiting high accuracy. Our methodology reduces the cost and time required for labeling while improving the model's functionality. It renders itself a viable choice for practical implementations.
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 2 The Principles of MALDI-TOF MS  
MALDI-TOF MS is an analytical technique that has become a cornerstone in microbial identification, particularly due to its speed, high sensitivity, and low sample-preparation requirements [37; 38; 39]. The principle of MALDI-TOF MS is based on the ionization of biomolecules--primarily ribosomal proteins--followed by analysis of their mass-to-charge (\(m/z\)) ratios using a time-of-flight detector system (Figure 1). The technique enablesthe generation of distinct spectral fingerprints, characteristic of specific microorganisms, which can be matched against comprehensive spectral databases for accurate taxonomic identification [40].
# Hybrid Methods for Selecting Precast Concrete Suppliers Based on Factory Capacity
## 6 Discussion
### Practies and Implementation  
The proposed framework is most effectively integrated into a multi-stage decision process that mirrors professional procurement practices. The first stage would involve an initial screening of all potential suppliers using a set of mandatory, non-negotiable \"pass/fail\" criteria. For the KSA precast industry, this would include verifying regulatory compliance, assessing financial stability, ensuring a minimum production capacity, and confirming a proven track record. This preliminary filtration efficiently ignores unsuitable candidates, creating a smaller, pre-qualified pool of viable suppliers.  
In the second stage, this shortlisted group of suppliers would undergo a detailed evaluation using the hybrid SWARA-PCA-WASPAS-VE framework developed in this paper. By applying a resource-intensive analysis to pre-qualified candidates only, decision-makers can conduct a more focused and rigorous assessment of the nuanced trade-offs between critical factors, such as cost, quality, and delivery performance.  
This two-stage approach enhances the scientific and practical value of the selection process. It ensures efficiency by focusing on deep analysis where it matters most. It guarantees that the final top-ranked supplier not only has the highest performance score but also meets all fundamental operational and commercial requirements. This methodology ensures that the final decision is both strategically sound and operationally reliable.
## 1 Introduction
Moreover, a novel data-gathering algorithm with joint CS and modified ant colony routing is presented. More specifically, the contributions of this paper are as follows. * Improved diffusion wavelets are used as the basis for data gathering, which makes full use of sensor nodes' spatial correlations;
* A sparse measurement matrix is used, where the non-zero components of each row denote the offspring nodes of one projection node, which only requires a fraction of nodes to participate in each measurement task, leading to a dramatic decrease in the energy consumption. * Modified ant colony routing compressively considering the next hop sensor node's residual energy and path length is proposed; to speed up the algorithm coverage ratio and avoid a local optimal, the pheromone impact factor is improved. Furthermore, a novel data-gathering scheme that integrates MST (Minimum Spanning Tree), modified ant colony routing, and compressive sensing is provided;
* Theoretical proof is shown on the equivalent measurement matrix to satisfy the RIP. * The superiority of our approach is demonstrated by numerical experiments with synthetic and real sensor data. The simulations show that our sparse basis sparsity the signal. In addition, our approach can accurately reconstruct the original signal, thereby reducing the energy consumption of WSNs and balancing the network load. The reminder of this paper is organized as follows. First, in Section 2, related work is introduced. Section 3 discusses preliminaries on CS theory and the system model. Section 4 analyzes a lot of WSNs datasets and signal spatial correlation characteristics. Furthermore, the proper sparse basis is designed using a modified diffusion wavelet. Moreover, it provides the modified ant colony routing algorithm. In addition, we describe a novel compressive data-gathering algorithm with joint improved ant colony algorithm and MST. Section 5 proves that the equivalent measurement matrix satisfies the RIP. The numerical results of synthetic and real datasets are analyzed in Section 6. Conclusions and future work are given in Section 7.
## 2 Literature Review
### Multi-Layer Perceptron (MLP) Network  
MLP are feed-forward neural networks with fully connected layers. This architecture is the most common and practical [21]. Typically, an MLP consists of three layers: an input layer, an output layer, and one or more hidden layers. The process begins with the input layer receiving data, followed by the hidden layer processing it and the output layer generating the output data. Each layer is connected to the next layer through weighted edges and biases. During the training process, the weights are adjusted to minimize the difference between the network's output and the desired output, thus allowing the MLP to learn [22; 23].  
Activation functions are strategically applied to the outputs of each node in our model to facilitate their transformation into non-linear outputs. The considered MLP model is illustrated in Figure 1, in which the seven scaled features are used as inputs. The hidden layers consist of neurons with rectified linear unit (ReLU) activation functions and a dropout rate of 0.2. The input layer is structured as \(X=(x_{0},x_{1},x_{2},\ldots,x_{n})\), and, through the following ReLU activation function, is used as follows:  
\[f(x)=max(0,x) \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Objectives** & **Techniques** & **Evaluation Metrics** & **Simulator** & **Application** \\ \hline
[13] & Localization & Location estimation technique & Range, DToA & Monte Carlo & IoT \\
[14] & Detection & KNN & CSI, OFDM & Commercial & Wi-Fi \\
[15] & Detection, & RSS and Number of Connected & & & \\  & Prevention & Neighbors (NCN) & RSS & Network simulator-2 & IoT \\
[16] & Detection & MAVLINK Dataset & \
\begin{tabular}{c} Fight \\ system parameters \\ \end{tabular} &
\begin{tabular}{c} PX4 autopilot and \\ Gazebo robotics \\ \end{tabular} & GPS \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._The network's output is computed based on the output of each unit. Specifically, the output of the hidden layer is calculated as follows:  
\[h_{i}^{j}=f\sum_{k=1}^{n_{i-1}}w_{k,j}^{i-1}h_{i-1}^{k},\ \ \ \ \ i=2,\ldots Nand\ \ j=1,\ldots n_{i} \tag{2}\]  
where \(h\) represents the output of neuron \(j\) in a hidden layer \(i\), \(f\) denotes the activation function, \(w\) signifies the weight between neurons \(k\) in hidden layer \(i+1\), and \(n\) corresponds to the number of neurons. The network's output is computed as follows:  
\[y_{i}=f\sum_{k=1}^{n_{N}}w_{k,j}^{N}h_{N,}^{k},\ \ \ \ \ y=y_{1},\ldots,y_{j}, \ldots,y_{N+1}=F(w,x) \tag{3}\]  
where \(Y\) represents the vector of the output layer and \(F\) denotes the transformation function.
# ACE: A Routing Algorithm Based on Autonomous Channel Scheduling for Bluetooth Mesh Network
## 6 Evaluation
### Impact of Traffic Load  
Now we investigate the impact of traffic load. Figures 7 shows PDR metric when traffic load varies from 1 pkts/s to 10 pkts/s. The results in Figure 7 show that the PDR performance of both Bluetooth Mesh and ACE is rapidly degraded as traffic load increases from 1 pkts/s to 10 pkts/s. Bluetooth Mesh has a slightly higher PDR when network traffic is low due to its multi-path feature at the cost of packet redundancy. However, ACE maintains a relatively high PDR with a heavy traffic load. This is because the total amount of traffic in Bluetooth Mesh is heavier than ACE due to its managed-flooding scheme. Moreover, three ADV channels in Bluetooth Mesh are severely congested and prone to have packets collisions with such heavy traffic. In contrast, ACE using its channel-varying routing algorithm not only can reduce the number of packets in the whole network but also avoid the blind time caused by switching scanning channels or receiving duplicate packets. Overall, under a dense node deployment and a heavy traffic load (10 pkts/s), compared to Bluetooth Mesh, ACE achieves 30% higher PDR. Therefore, ACE can improve network reliability for applications with dense traffic.
## 2 Kinetic Similarity between SG and GSC  
Through comparison, it can be seen that the GSC of the PMSG-based wind turbine and the SG have similar dynamic equations, as shown in Figure 1, where the symbols with superscripts in equations are represented in per-unit value, \(H_{C}\) is the inertia constant of the DC-link capacitor, \(\overline{u}_{dc0}\) is the steady-state DC-link voltage, \(\overline{u}_{dc}\) is the DC-link voltage, \(\overline{P}_{m}\) is the output power of the machine side converter (MSC), \(\overline{P}_{g}\) is the output power of the GSC, \(\overline{U}_{t}\) is the modulation voltage amplitude of the GSC, \(\overline{E}_{g}\) is the grid voltage amplitude, \(\overline{x}\) is the grid reactor, \(\delta\) is the phase difference between the GSC's output voltage and the grid voltage, C is the DC-link capacitance, U\({}_{\text{dcn}}\) is the rated DC-link voltage, \(S_{\text{n}}\) is the rated capacity of the wind turbine, \(H_{J}\) is the inertia constant of the SG's rotor, \(\overline{\omega}_{m}\) is the speed of the SG, \(\overline{P}_{M}\) is the input power of the SG, \(\overline{P}_{e}\) is the electrical power of the SG, \(\overline{\psi}\) is the magnetic linkage of the SG, \(\overline{x}_{G}\) is the equivalent reactance, and \(\delta_{G}\) is the power angle of the SG.  
In Figure 1, the DC-link voltage of the GSC has a similar dynamic equation with the rotated speed of the SG. In the same way, the modulation voltage amplitude of the GSC shows similar dynamics compared with the magnetic linkage of the SG. Similarly, the inertia constant, \(H_{C}\), of the capacitor also exhibits similar dynamic characteristics to the inertia constant, \(H_{J}\), of the SG's rotor. These above similarity relationships between the GSC and the SG are shown in Figure 2. It should be noted that these physical quantities in Figure 2 have similar dynamic characteristics, but they are not equal.
# An Opportunistic Network Routing Algorithm Based on Cosine Similarity of Data Packets between Nodes
## 4 Experiments and Results
The delivery delay of the cosSim algorithm is affected minimally by the change of the number of nodes, and the S&W algorithm and the Epidemic algorithm show the same growth trend. Figure 8: Delivery ratio. Figure 7: Delivery ratio (node cache). When the number of nodes is 600, the delivery delay of Epidemic algorithm and S&W algorithm is as high as 6500 and 5500 respectively. The delivery delay of the cosSim algorithm is slow. After the number of nodes is greater than 400, the delivery delay of the cosSim algorithm is maintained at around 3000, which is less than 2500 for the S&W algorithm and less than half for the Epidemic algorithm. Figure 10 shows the effect of the number of nodes on the routing overhead of the three algorithms. As the number of nodes increases, the routing overhead of the three algorithms presents different growth trends. Among them, the Epidemic algorithm has the fastest growth rate of routing overhead, which is about twice the S&W algorithm and about three times that of the cosSim algorithm. When the number of nodes in the network is as high as 600, the routing overhead of the S&W algorithm is around 2500, that of the Epidemic algorithm is around 4500, and the routing overhead of the cosSim algorithm is the lowest around 1500. Because the Epidemic algorithm is based on the flooding strategy, it will generate a large number of copies in the network, which is likely to cause too much routing overhead. The above simulation results show that compared with the traditional routing algorithms, S&W algorithm and Epidemic algorithm, the cosSim algorithm is more suitable for the data transmission of the OppNet, which can effectively improve the delivered ratio, and reduce the delivery delay and the routing overhead. Figure 10: Routing overhead. Figure 9: Delivery delay.
# CPM-OFDM Performance over Underwater Acoustic Channels
## 4 Shallow Underwater Acoustic Channel Model
### Deterministic Response  
In an underwater medium the energy of an acoustic signal gets attenuated both as a function of distance as well as frequency, and the pathloss is a combination of geometric spreading as well as absorption. Mathematically, if the variation in time is \(e^{\beta\omega t}\) the path frequency domain transmission loss expression in the positive \(z\) direction will be:  
\[E(z)=E_{0}\,e^{-\gamma z}=E_{0}\,e^{-\alpha z}\,e^{-\beta z} \tag{9}\]  
where a scaling constant is denoted by \(E_{0}\). The channel's deterministic transfer function \(H_{a}(f,d)\)[49; 50] is:  
\[H_{a}(f,d)=A_{d}\,e^{-\gamma(f)d} \tag{10}\]  
Figure 5: Shallow underwater acoustic channel model.  
where \(A_{d}\) and \(d\) are a scaling constant and transmitter-receiver distance, respectively, while \(\gamma\) denotes a complex propagation constant, in m\({}^{-1}\):  
\[\gamma(f)=\alpha(f)+j\beta(f) \tag{11}\]  
where \(\alpha(f)\) and \(\beta(f)\) are absorption and phase constants, respectively. In UWA channels the absorption is a function of frequency as acoustic pressure is reduced due to energy losses in heat. According to the Thorp formula [47], the absorption coefficient in computed in dB/km as:  
\[\alpha_{dB}(f)=1.094\bigg{(}0.003+\frac{0.1f^{2}}{1+f^{2}}+\frac{40f^{2}}{410 0+f^{2}}+0.000275f^{2}\bigg{)} \tag{12}\]  
Figure 6a shows, for multiple distances, the absorption pathloss transfer function. Similarly, phase constant in rad/m is:  
\[\beta(f)=\frac{2\pi f}{c_{s}} \tag{13}\]  
where \(c_{s}\) represents sound velocity [51] in ms\({}^{-1}\), calculated as:  
\[c_{s}=1449.2+4.6T-0.0557^{2}+0.00029T^{3}+((1.34-0.01)T)(S-35) \tag{14}\]  
where \(T\), \(S\), and \(Z_{M}\) are temperature, salinity, and depth, respectively. Equation (14) is called [PERSON]'s equation [51]. The realistic channel values are considered with temperature between 0 to 35 \({}^{\circ}\)C while salinity is from 0 to 45 PPT and the supported depth is up to 1000 m. The signal \(c_{1}(t)\) in Figure 5 can be expressed as:  
\[c_{1}(t)=h_{a}(t,d)\ \bigotimes\ s(t) \tag{15}\]  
where \(h_{a}(t,d)\) represents the IFFT of the channel transfer function convolved with the transmitted signal \(s(t)\).
## Materials and Methods
### Protein Extraction, 2-D DIGE and MALDI TOF/TOF MS Analysis  
The proteins from SRPs were extracted as described [12; 23]. DIGE analysis was performed to determine DAPs as described [12]. Proteins of SRPs from the D24, D48, E24, and E48 latex were labeled with a ratio of 400 pmol Cy3 or Cy5. Cy2 was used to label the internal standard produced by pooling an equal protein quantity from the four samples. Differential in-gel analysis module was used for spot detection, and biological variation analysis module was employed in the three biological replicates to identify DAPs with higher than 99% confidence (\(p<0.01\), \(t\)-test value \(<0.05\)). Furthermore, these DAPs were manually excised and in-gel digested with bovine trypsin as described [12; 15]. Mass spectra of the peptides were acquired on an AB 5800 MALDI TOF/TOF MS instrument as described [12; 15]. These peptide masses were searched against the _Hevea_ genome scaffolds (BioProject ID: PRJNA80191, www.ncbi.nlm.nih.gov/nuccore/448814761) and the draft genome (GenBank: AJJZ01000000) with 46,718 sequences and 17,435,757 residues [8]. Positive identification was based on a Mascot score higher than 75 (\(p<0.001\)), at least 2 matched peptides, and a maximum peptide coverage higher than 5%. Detailed information about protein identification is provided in Table S1.
# Harmonic Detection Method Based on Parameter Optimization VMD-IWT Combined Noise Reduction
###### Abstract  
Aiming at the problem that the existing harmonic detection methods are susceptible to noise interference in the actual working environment, which leads to the reduction of detection accuracy, this paper introduces a novel harmonic detection technique utilizing a RIME optimization algorithm (RIME) to enhance variational mode decomposition (VMD) combined with an improved wavelet threshold (WT) approach. Initially, RIME optimization refines VMD for the decomposition of harmonic current signals, yielding several modal components. Subsequently, a correlation coefficient method distinguishes between effective and ineffective modal components, discarding the latter. The effective components undergo noise reduction through an enhanced wavelet thresholding technique, and these denoised components are then reconstructed to produce the final noise-reduced signal. Finally, the Hilbert transform is applied to the denoised signal to extract harmonic parameters. Verification through both simulated and actual signal measurements demonstrates that the proposed method not only surpasses other noise reduction algorithms in signal-to-noise ratio and root-mean-square error, but also shows superior accuracy and robustness compared to alternative detection techniques. This method effectively filters out signal noise under noise interference, minimizes detection errors, and achieves precise harmonic signal detection with improved accuracy.  
variational mode decomposition; optimization algorithm; wavelet thresholding; harmonic detection +
Footnote †: journal: Applied sciences
## 2 Models and Methods
### Modal Reduction through the Craig-Bampton Method  
In some situations, it could be convenient to reduce the number of degrees of freedom involved in the dynamic model of a system, for example, in the case of the finite element models of the linkages introduced in Sections 2.2.2 and 2.2.3, where only the physical DoFs that connect one component with the other components are retained.  
For this purpose, the Craig-Bampton approach [40] can be used. Considering a given subsystem \((r)\), the full set of DoFs \(\boldsymbol{u}^{(r)}\) can be partitioned as:  
\[\boldsymbol{u}^{(r)}=\left\{\begin{array}{l}\boldsymbol{u}_{b}\\ \boldsymbol{u}_{i}\end{array}\right\} \tag{15}\]where subscripts \(b\) and \(i\) indicate boundary and interior DoFs, respectively, and the superscript (\(r\)) is dropped. Note that the \(r\)th substructure is connected to the other substructures through the boundary DoFs. The displacements \(\mathbf{u}^{(r)}\) can be approximated considering the static deformation modes \(\mathbf{\Phi}_{\mathbf{c}}\) and the fixed interface modes \(\mathbf{\Phi}\) as follows:  
\[\mathbf{u}^{(r)}=\begin{cases}\mathbf{u}_{\mathbf{b}}\\ \mathbf{u}_{i}\end{cases}\simeq\begin{bmatrix}\mathbf{\Phi}_{\mathbf{c}}&\mathbf{\Phi}\end{bmatrix} \begin{cases}\mathbf{u}_{\mathbf{b}}\\ \mathbf{q}_{\mathbf{m}}\end{cases} \tag{16}\]  
where \(\mathbf{q}_{\mathbf{m}}\) represents the modal coordinates associated with the fixed interface modes.  
Static deformation modes are expressed as:  
\[\mathbf{\Phi}_{\mathbf{c}}=\begin{bmatrix}\mathbf{\mathrm{I}}_{\mathbf{\mathrm{b}}}\\ \mathbf{\Phi}_{\mathbf{c},i}\end{bmatrix} \tag{17}\]  
where each column \(b\) of the sub-matrix \(\mathbf{\Phi}_{\mathbf{c},i}\) represents the static deformation of the interior DoFs when a unit displacement is imposed on the boundary DoF \(b\).  
Fixed interface modes are expressed as:  
\[\mathbf{\Phi}=\begin{bmatrix}\mathbf{\mathrm{b}}_{\mathbf{\mathrm{b}}}\\ \mathbf{\Phi}_{i}\end{bmatrix}. \tag{18}\]  
Generally, a truncated set of \(m\) fixed interface modes, with \(m\ll i\), is used to approximate the dynamics in a limited frequency band.  
Therefore,  
\[\mathbf{u}^{(r)}\simeq\begin{bmatrix}\mathbf{\mathrm{I}}_{\mathbf{\mathrm{b}}}&\mathbf{ \mathrm{b}}_{\mathbf{\mathrm{b}}}\\ \mathbf{\Phi}_{\mathbf{c},i}&\mathbf{\Phi}_{i}\end{bmatrix}\begin{cases}\mathbf{u}_{\mathbf{b}}\\ \mathbf{q}_{\mathbf{m}}\end{cases}=\mathbf{\Gamma}^{(r)}\mathbf{q}^{(r)} \tag{19}\]  
where \(\mathbf{\Gamma}^{(r)}\) indicates the transformation matrix associated with the reduced set of generalized coordinates \(\mathbf{q}^{(r)}\).  
By using the coordinate transformation of Equation (19) in Equation (1) and premultiplying by \(\mathbf{\Gamma}^{(r)^{T}}\), one obtains:  
\[\mathbf{\hat{M}}^{(r)}\mathbf{\bar{q}}(t)^{(r)}+\mathbf{\hat{C}}^{(r)}\mathbf{\hat{q}}(t)^{(r )}+\mathbf{\hat{K}}^{(r)}\mathbf{q}(t)^{(r)}=\mathbf{\hat{f}}(t)^{(r)}+\mathbf{\hat{g}}(t)^{(r)} \tag{20}\]  
which approximates the equation of motion in the reduced set of generalized coordinates \(\mathbf{q}^{(r)}\).