esa-sceva/satcom-chunk-collection huggingface dataset:
# Labeling Expert: A New Multi-Network Anomaly Detection Architecture Based on LNN-RLSTM
## 8 Evaluation
### Real Exceptions for Data Labels  
The quartile method is only suitable for testing the most obvious anomalous data, while Random Forest, SVM [42] and LightGBM cannot effectively check the anomalous frequencies [49]. As shown in Table 1, for the average number of users in the cell, the quartile method has the worst detection effect on the dataset and performs better on other datasets, which, to some extent, can also explain why LNN-RLSTM has the smallest F-Score for the average number of users dataset in the previous experiments. Further, we can  
Figure 14: Time loss for training new type of anomalies added manually.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Categories** & **Technique** & **Year** & **Domain** & **Ref** & **Time Complexity** \\ \hline Supervised & Naive Bayes & 1960 & Cybersecurity & [42] & \(\mathrm{O(mn)}^{2}\) \\ Supervised & Random forest & 2001 & Cybersecurity & [43] & \(\mathrm{O(O(Mmlogn))}^{3}\) \\ Supervised & ANN & 2000 & Cybersecurity & [44] & \(\mathrm{O(emnk)}^{4}\) \\ Supervised & SVM & 2011 & Email Spam & [45] & \(\mathrm{O(n^{2})}^{1}\) \\ Unsupervised & DBN & 2016 & Email Spam & [46] & \(\mathrm{O((n+N)k)}^{7}\) \\ Supervised & Decision Tree & 2016 & Email Spam & [47] & \(\mathrm{O(mn^{2})}^{5}\) \\ Supervised & SVM & 2018 & Spam Tweets & [48] & \(\mathrm{O(n^{2})}^{1}\) \\ Unsupervised & K-means & 2007 & Software & [49] & \(\mathrm{O(I\times n\times k\times m)}\) \\ Semi-supervised & LNN & 2022 & Operational exception & - & \(\mathrm{O(n\times m\times k\times v)}\) \\ Semi-supervised & RLSTM & 2022 & Operational exception & - & \(\mathrm{O(4(nm+n^{2}+n))}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Time complexity comparison.  
see the limitations of the quartile method, so it can only be used as a tool for auxiliary LNN-RLSTM training.
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 5 Simulation and Results
### Performance Analysis of Tbrl with Dbr and R-ERP\({}^{2}\)R
#### 5.4.1 End to End Delay  
A comparison against the time needed to create a path from source to the destination node for sending data packets is shown in Figure 13. In some simulation results, DBR consumes less time when compared to TBRL, whereas, in remaining results, it consumes high time. This is caused by the greedy approach that is adopted by DBR. Sometimes favorable results are obtained through DBR. After taking the average of favorable and not favorable results, values have been placed against TBRL. In R-ER\(P^{2}\)R, a maximum range of physical distance is not set, it takes greedy approach to choose the next one and  
Figure 12: Packet Delivery Ratio of 2N-RM, 3H-RM, and 2H-ACK.  
only makes one path for sending the data. In TBRL, physical distance is set and it takes into account multiple possible paths from source to sink, after that it sorts the path and chooses the optimal one in terms of delay.
## 5 In-Line Application of LIBS in Industrial Production
### Coal and Coal Ash
(**b**) Real-time coal quality monitoring by LIBS (triangles) and PGNAA (squares). Reference concentrations (circles). Adapted from [110]. as a secondary raw material. A high concentration of unburned carbon can have adverse impact on the combustion efficiency and on the value of the ash. The carbon concentration is presently measured off-line by manually obtaining fly ash samples from the precipitator ash hoppers or flue gas streams and sending the samples to a laboratory. Depending on the laboratory test procedure employed, the results may not be available for up to 24 h. This procedure takes several hours and delays the combustion optimization process. For online measurements a LIBS system and a two-stage cyclone measurement system were developed to quantify the carbon content in fly ash in real-time [106]. The cyclone system in combination with a 1 ns pulse-width laser enabled to eliminate the effect of CO\({}_{2}\) on the unburned carbon content. A schematic diagram of a boiler control system using LIBS in a coal-fired power plant for fly ash measurement is shown in Figure 7a. The unburned carbon content in different fly ash samples as measured by LIBS and by standard chemical analysis methods (Japanese Industrial Standards JIS) are compared in Figure 7b. For improved quantitative analysis the plasma temperature correction method was used. The results of measurements by LIBS and JIS method were consistent with R\({}^{2}\) = 0.9052 and RMSEP = 3.9% in the measurement range of 14.0 to 53.2% of unburned carbon. This demonstrates the feasibility of LIBS for real-time measurement of fly ash contents in power plants. Some earlier studies on the on-line analysis of unburned carbon in fly ash and of combustion products in industrial boilers and furnaces are reported in [454,455], respectively.
# Humans and Automation: Augmenting Security Operation Centers
## 3 Results and Findings  
The study selection process yielded 49 articles published over the last 11 years. It is worth noting that no date limitations were applied when selecting literature, but it just so happened that the selected literature dates between 2013 and 2024. While the literature on SOCs before 2013 is available, discussions centered on how automation is used and its implications on analysts are part of an emerging field. Available data indicate that since 2019, there has been a rapid increase in literature examining the specificities of automation in security operations. Figure 2 shows the number of publications by year.  
The final 49 articles were imported in NVivo, read in full, and thematically analyzed. The thematic analysis involves identifying themes and patterns in the literature and categorizing them accordingly [22]. We followed the six-phase approach, which produces both semantic (themes that directly present themselves in the literature) and latent (themes that are developed based on the interpretation of data) themes [22]. This resulted in 609 coding references being coded against 65 individual codes and further distributed across four main themes: (1) SOC Automation Application Areas, (2) SOC Automation Implications, (3) Human Factor Sentiment, and (4) SOC Challenges Necessitating Automation (seen in Figure 3).  
Figure 1: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) diagram.  
Figure 3 displays the result of the final coding process, with each block illustrating the proportion of each theme in relation to the 609 coding references. For instance, automation application areas mark the most dominant theme discussed in the literature. Similarly, there is room for more research to address the human factor sentiment in SOCs, discussing analysts' tacit domain knowledge, their role as decision-makers, and how to best team them with AI tools and technologies.
###### Abstract
In this study, the method considering both intra-particle and fluid-to-solid film resistances is referred to as the rigorous diffusion technique (R technique). If the method only considers intra-particle resistance, it is referred to as the simplified diffusion technique (S technique). In general, the results obtained from the R technique are fairly accurate. However, the R technique is a very complicated and time-consuming approach and is not convenient for practical use. The S technique provides a simpler approach, but it is only valid in cases when the contribution of fluid film resistance is negligible [17]. Therefore, the efficient simplified diffusion technique (ES technique) for the determination of surface diffusivity that relies on a completely mixed batch reactor is proposed in this study to overcome the limitations of the S technique. The estimation errors of the ES technique are significantly smaller than that of the S technique when compared with the R technique. The proposed ES technique would be useful for field applications to determine the surface diffusivity for an aqueous adsorption system.
## 4 Conclusions  
In this paper, a new pedestrian semantic attributes dataset named NEU-dataset is built, and a new key pedestrian semantic attributes recognition algorithm based on the joint activation function and dynamic weight is proposed. The proposed method improves the DeepMAR method by setting a new loss function. Sigmoid and Softmax loss are combined to solve multi-label problems, and dynamic weight is applied into the loss function to solve the unbalanced samples. The experimental results have shown that our proposed method is effective in pedestrian semantic attributes recognition. In addition, our method has good performance in the NEU-dataset and RAP dataset in the mA, Acc and Rec indexes. In the future, new effective loss functions can be proposed to solve multi-attribute and multi-class problems. For these problems, the structure of the proposed network can also be applied and expanded.  
However, for objects occupying very small regions in the image, e.g., the glasses and most regions of objects occluded in the image, e.g., Handbag, the proposed network can extract features of the entire image. The global features are not able to distinguish these attributes represented by a few regions. For these problems, the attention mechanism and multi-level feature extraction methods for fine-grained features and local significant information representation can be added to the network.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & \(mA\) & \(Acc\) & _Prec_ & _Rec_ & \(F1\) \\ \hline
**ELF-mm** & 69.94 & 29.29 & 32.84 & 71.18 & 44.95 \\
**FC7-mm** & 72.28 & 31.72 & 35.75 & 71.18 & 47.73 \\
**FC6-mm** & 73.32 & 33.37 & 37.57 & 73.23 & 49.66 \\
**CAN** & 69.66 & 62.61 & **80.12** & 72.26 & 75.98 \\
**DeepMAR** & 73.79 & 62.02 & 74.92 & 76.21 & 75.56 \\
**M-Net** & 74.44 & 64.99 & 77.83 & 77.89 & 77.86 \\
**HP-Net** & 76.12 & 65.39 & 77.33 & 78.79 & **78.05** \\
**Our method** & **89.94** & **88.27** & 52.15 & **95.33** & 65.12 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Attribute recognition results (%) comparison on RAP dataset. The bold number is the max value on each row except the last row.  
**Funding:** This research was funded by National Natural Science Foundation of China, grant number 41801241, and the National High Technology Research and Development Program of China (863 Program) (No. 2012 AA041402). The APC was funded by Y.W. and G.T.  
**Acknowledgments:** The authors would like to thank [PERSON] in Northeastern University for improving the English of this paper, and also thank [PERSON] et. al for providing the RAP dataset for academic research. Besides, the authors thank intelligent technology research center of Neusoft for the images capturing and processing.  
**Conflicts of Interest:** The authors declare no conflict of interest. The people with whose images the authors have worked have freely provided these data for academic research use.
## 2 Vibration Signal Processing Methods  
### Signal Denoising Processing Methods  
Denoising is performed using DWT, as shown in Figure 2. Three-level decomposition is first conducted using the Daubechies 3 mother wavelet, and denoising is then performed by  
Figure 1: Overall technical roadmap.  
soft thresholding. This results in dividing the vibration signal s(n) into two frequency sub-bands. s(n) is first passed through a low-pass filter h(n) and a high-pass filter g(n), followed by 2-fold down-sampling. This process converts it into approximation component CA1 and detail component CD1. Through multi-frequency analysis, the detail components CD and approximation components CA of the three levels of decomposition are determined as [44]:  
\[\text{s(n)}=CD1+CD2+CD3+CA3 \tag{1}\]  
The soft thresholding is expressed as [46]:  
\[\delta_{\text{s}}(x,\lambda)=\left\{\begin{aligned} \text{sgn}(x)(x-\lambda)|x|\geq \lambda\\ 0|x|\leq\lambda\end{aligned}\right. \tag{2}\]  
where \(\lambda\) is the threshold, and x is the denoised detail coefficient. \(\lambda\) is computed as \(\lambda=\sqrt{2\log_{e}M}\), where \(M\) is the number of data points.
## 2 Architecture: The EMERGE System
### Services and Computational Tasks  
Given the above organization and architecture, the following are identified as the three primary services provided by the EMERGE system and its fleet and currently (either fully or partially) supported by the deployed prototype under test:  
1. _Vehicle task planning:_ Given a set of \"goal\" operations to be performed by vehicles in the fleet in a geographical zone and in specified time windows of the day (e.g., pickup or delivery operations), provided as input, the system determines a detailed scheduling plan for each vehicle, including routes to be followed, departure or waiting times, the  
Figure 2: The software architecture of the EMERGE system.  
order of the locations to visit, and items to carry, to allow the vehicles to accomplish all goals while optimizing metrics of interest (e.g., fuel consumption);
2. _Network monitoring:_ Continuous, real-time, data acquisition is performed by vehicles moving in the given geographical zone of interest through their sensor equipment for monitoring (and predicting) the status of the network over time (traffic, disruptions, congestions, detours);
3. _Ract and reoptimize:_ To respond to changes in the status of the network (e.g., disruptions, roadworks, emergency vehicles) or that of the vehicles (e.g., malfunctions), vehicles coordinate and cooperate (by communicating and exchanging data both with the infrastructure and between each other) in order to update/reoptimize the scheduling plan to consider status changes that have occurred while continuing with the assigned operations.  
In order to provide these services, appropriate data models, computational problems, and algorithms have been identified, studied, and implemented to be part of the software architecture of the EMERGE system, respectively.
## 4 Experimental Set-Up
### Datasets
#### 4.1.1 Labeled Datasets  
CorpusCine is a dataset formed by 3878 Spanish-written movie reviews captured from the MuchoCine website ([[https://muchocine.net/](https://muchocine.net/)]([https://muchocine.net/](https://muchocine.net/)), accessed on 31 March 2021). Each document is rated using an integer tag ranging from 1 (unpleasant movie) to 5 (excellent movie). The rating distribution for the CorpusCine dataset is presented in Table 1. We remark that using such a dataset for sentiment analysis configures a multi-class classification problem; however, as we have clarified in Section 3, our sentiment analysis approach was designed for binary settings (positive and negative documents). Accordingly, we use the procedure in [45] aiming to convert the CorpusCine into a binary problem, as follows. Documents with a rating of one or two are considered a negative review (\(-1\)); conversely, manuscripts with a label of four and five are estimated as positives (1). We discard documents with a tag of three because they correspond to neutral or mixed opinions [46]. This dataset is publicly available ([[http://www.lsi.us.es/~fermin/corpusCine.zip](http://www.lsi.us.es/~fermin/corpusCine.zip)]([http://www.lsi.us.es/~fermin/corpusCine.zip](http://www.lsi.us.es/~fermin/corpusCine.zip)), accessed on 31 March 2021).  
On the other hand, we use the PaperReviews corpus, which comprises paper reviews sent to an international conference in Spanish. The corpus has a total of 382 documents labeled using a five-point scale, indicating the opinion about the paper quality. Table 1 shows the rating distribution for the PaperReviews dataset. Alike for CorpusCine, we use a methodology to treat PaperReviews as a binary classification problem. Samples with ratings of one or two are considered negative reviews; similarly, documents with ratings of four or five are categorized as positive reviews. PaperReviews is publicly available ([[https://archive.ics.uci.edu/ml/datasets/Paper+Reviews](https://archive.ics.uci.edu/ml/datasets/Paper+Reviews)]([https://archive.ics.uci.edu/ml/datasets/Paper+Reviews](https://archive.ics.uci.edu/ml/datasets/Paper+Reviews)), accessed on 31 March 2021).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Rating**} & \multicolumn{3}{c}{**Number of Documents**} \\ \cline{2-3}  & **CorpusCine** & **PaperReview** \\ \hline
1 & 351 & 36 \\
2 & 923 & 136 \\
3 & 1253 & 104 \\
4 & 890 & 91 \\
5 & 461 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rating distribution for the labeled corpora.
# Array Gain of a Linear Array in an Ocean Waveguide
## 2 Array Gain for Plane Wave  
The array gain for an arbitrary array is defined as [2]:  
\[AG=\frac{SNR_{\text{array}}}{SNR_{\text{hyp}}}, \tag{1}\]  
where \(SNR_{\text{array}}\) is the \(SNR\) at the array output, and \(SNR_{\text{hyp}}\) is that at a single element of the array. The traditional formula given by [PERSON] calculates \(AG\) as:  
\[AG_{\text{U}}=\sum\limits_{i=1}^{M}\sum\limits_{k=1}^{M}(\rho_{s})_{ik}/\sum \limits_{i=1}^{M}\sum\limits_{k=1}^{M}(\rho_{n})_{ik}, \tag{2}\]  
where \((\rho_{s})_{ik}\) and \((\rho_{n})_{ik}\) are the signal and noise correlation coefficients between the \(i\)th and \(k\)th elements, respectively. \(M\) is the number of elements. The subscript \"U\" indicates that this formula was given by [PERSON]. If the ambient noise is uncorrelated, \(AG_{\text{U}}=\frac{1}{M}\sum\limits_{i=1}^{M}\sum\limits_{k=1}^{M}(\rho_{s} )_{ik}\). \(AG_{\text{U}}\) can be only applied to the scenarios where both the signal powers and the noise powers are equal at their respective elements. However, this is not true in the real ocean as: (1) the propagation losses are not equal, and this inequality is more pronounced in the transition area between the shadow zone and the high intensity zone; (2) the noise power distribution is depth-dependent, particularly in deep sea [20]. In addition, Equation (2) does not take into account the effect of the ocean waveguide on the array performance. In the following, a theoretical formula for \(AG\) is derived in terms of the spatial-coherence of the acoustic channels and propagation losses that can help to interpret the degradation of \(AG\) caused by the acoustic channel fluctuation.
## 4 Discussion
### Node Residual Energy  
Next, we provide a detailed analysis of the energy optimization of cognitive nodes in this algorithm. As shown in Figure 6, nodes \(S_{1}\) and \(S_{2}\), when sufficiently energized, transmit at the optimal center frequency power of \(P_{lim1}\) in Formula (22). Nodes \(S_{3}\) and \(S_{4}\), when sufficiently energized, transmit at the optimal non-center frequency power of \(P_{lim2}\) in Formula (22). Node \(S_{1}\), due to its greater distance from the receiving end of other nodes, experiences less interference from other nodes, allowing it to achieve a larger transmission power according to Formula (22). However, node \(S_{4}\) is relatively close to the other receiving end of other nodes and experiences greater interference from other nodes, allowing it to achieve a smaller transmission power according to Formula (22). When the remaining energy of node \(S_{4}\) reaches about 40%, as nodes \(S_{1}\) and \(S_{3}\) exit the game, the interference of node \(S_{4}\) by other nodes decreases, and the transmitting power will suddenly increase.  
In summary, during the initial period, nodes do not consume energy, and their remaining energy is 100%. The transmission power of all nodes reaches the Nash equilibrium point. As the remaining energy of nodes decreases, their transmission power gradually decreases. Formula (11) in this study shows that as energy (\(E\)) decreases, the price of unit power increases, and the user's transmission cost also increases. Therefore, according to this algorithm, users will reduce their transmission power, thereby increasing the total revenue \(U(P_{k})\) of nodes and saving energy. Thus, this algorithm can control the transmission power of nodes based on the remaining energy, ultimately enhancing the overall network performance.  
With the increase in network load, the system capacity of the three algorithms increases continuously. When the network load is not high, the system capacity differences among the three algorithms are not significant.  
Figure 5: System capacity under different algorithms.
# RobustQuote: Using Reference Images for Adversarial Robustness
## 4 Experiments
### Ablation and Parameter Sensitivity Studies
#### 4.3.2 \(\tau\) to Determine the Effect of \(\mathcal{L}_{\text{rect}}\)  
In the following section, we explore the impact of RobustQuote's hyperparameters, \(\tau\) and \(\lambda\), as well as where to place a RobustQuote block and how many to use.  
Starting from \(\tau\), we report the scores for different values in Table 6 and visualize them in Figure 5a. We see a clear best value at \(\tau=0.5\), outperforming all other values in adversarial attacks by up to 0.7% in C&W, while lagging by only 0.1 in the worst case in AutoAttack. Overall, \(\tau=0.5\) provides a 0.05% average benefit over the second-best choice of \(\tau=1.0\). It seems that values lower than 0.5 (imposing a greater difference of correction) have diminishing returns and it becomes too hard to find large adversarial corrections \(\|\hat{x}_{p}^{\prime}\|_{2}\) relative to \(\|\hat{x}_{p}\|_{2}\). On the other hand, a larger \(\tau\), which hardly imposes a constraint on lengths, yields a lower performance than \(\tau=0.5\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\tau\) & **Natural** & **PGD-20** & **PGD-100** & **C\&W** & **AA** \\ \hline
1.0 & 78.23 & 59.56 & 59.94 & 59.88 & 47.32 \\
0.9 & 78.50 & 59.28 & 59.74 & 59.71 & 47.40 \\
0.5 & 78.54 & 59.34 & 60.10 & 59.87 & 47.26 \\
0.1 & 78.26 & 59.21 & 60.11 & 59.12 & 47.22 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation of the impact of using different temperatures \(\tau\) to control the rectification amount. The results are computed on a DeiT3-T backbone using the CIFAR10 dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rectifier** & **Natural** & **PGD-20** & **PGD-100** & **C\&W** & **AA** \\ \hline None & 78.88 & 59.33 & 59.63 & 59.53 & 47.24 \\ Random & 78.88 & 58.96 & 59.98 & 59.84 & 47.17 \\ Self & 78.11 & 59.28 & 59.30 & 59.49 & 47.27 \\ RSA & 78.71 & 59.53 & 60.07 & 59.77 & 46.85 \\ Rec & 78.54 & 59.34 & 60.10 & 59.87 & 47.26 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation of the impact of using different design choices for the rectification module. The results are computed on a DeiT3-T backbone using the CIFAR10 dataset.  
Figure 5: Hyperparameter search. The average accuracies are calculated in Natural, PGD-20, PGD-100, C&W-20, and AutoAttack. (**a**) Search of the best \(\tau\), Table 6. (**b**) Search of best \(\lambda\), Table 7. (**c**) Search of best Block Id, Table 8.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\lambda\) & **Natural** & **PGD-20** & **PGD-100** & **C\&W** & **AA** \\ \hline
0.9 & 70.25 & 51.68 & 52.65 & 52.59 & 37.65 \\
0.5 & 76.27 & 58.58 & 58.79 & 58.58 & 45.01 \\
0.25 & 78.42 & 58.96 & 59.73 & 59.07 & 46.66 \\
0.1 & 78.54 & 59.34 & 60.10 & 59.87 & 47.26 \\
0.0 & 78.56 & 59.47 & 59.83 & 59.36 & 47.23 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation of the impact of using different values of \(\lambda\). The results are computed on a DeiT3-T backbone using the CIFAR10 dataset.