esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Stability Prediction in Peripheral
## 3 Results
### Accuracy and Computation Times  
Since the convergence of the eigenvalues depends on parametric values \((n,a_{p})\), a more general approach has been studied using an \(11\times 11\) matrix containing an equally-spaced sweep of spindle speeds (2000 rpm to 7500 rpm) and axial depths of cut (0 to 10 mm). The difference between the matrix's approximate critical eigenvalues \(\upmu\) and exact ones \(\upmu_{0}\) is presented as the function of the number of discretizations, or subintervals. The exact matrix of the eigenvalues \(\upmu_{0}\) was determined by each corresponding method with \(N=200\). Figure 6 shows the results for 25% radial immersion. Note that this figure does not compare the converged values between methods, as no method converges at the same eigenvalues.  
Another important parameter is the time it takes to compute the stability lobes. Here, the average time needed to compute an eigenvalue for a combination of parameters \((n,a_{p})\) is used, which was calculated over the \(11\times 11\) matrix used previously. Figure 7 shows the average time for each method as a function of the number of discretization or sub-intervals. Once again, it is clear that the fastest methods for the stability performed were the CH and EMHP methods. It is important to note that the immersion does not change the number of mathematical operations. Figure 7 is valid for any value of radial immersion.  
Figure 6: Convergence of the norm of the \(11\times 11\) matrix of eigenvalues for all methods.
# Exploring ISAC: Information-Theoretic Insights
## 5 Network ISAC with Sensing Distortion  
Modern communication systems are often multi-user and network-oriented, meaning that multiple Txs wish to simultaneously transmit data to multiple Rxs and some of these terminals have to accomplish sensing tasks. Characterizing the information-theoretic fundamental limits of multi-user network systems has been an active area of research for decades [92], and a vast majority of the systems still lack complete and computable characterizations of the fundamental performance limits, even when only data have to be transmitted, i.e., for systems without sensing tasks. Nevertheless, different interesting and insightful code constructions have been proposed for network communication systems and it has been shown that they perform reasonably close to the fundamental limits. In recent studies, researchers have introduced sensing tasks into these code constructions to obtain information-theoretic network ISAC schemes. Information-theoretic converse (infeasibility) results have also been derived for certain network ISAC scenarios. In this section, we shall review both network ISAC coding schemes and converse results. Note that network ISAC has also received significant attention in the signal processing and communication theory literature. We refer to [33; 93] for these results.  
We will start by reviewing a broadcast ISAC where communication is from a single Tx to multiple Rxs and the sensing is performed at the Tx. As we shall see, the sensing problem is the same as in the point-to-point communication scenario, and thus, the simple symbolwise estimator in (5) is optimal, so that the sensing problem and the communication problem \"decouple\" similarly to in the point-to-point case. The second scenario that we consider in this section is the multi-access ISAC problem with Tx sensing. Since the sensing task is accomplished at multiple distributed terminals with heterogeneous sensing information, this sensing problem is fundamentally different and allows for more complicated strategies, e.g., collaborative sensing strategies and the interactive exchange of sensing information between the different terminals. We shall present different ISAC coding schemes that perform the required communication tasks, and at the same time also exchange sensing information, thus allowing the implementation of collaborative sensing strategies. Similar strategies have also been proposed for device-to-device (D2D) communication (the two-way channel) and the interference channel (IC).
# Hybrid CNN-NLP Model for Detecting LSB Steganography in Digital Images
## 5 Discussion
In the case where the method is applied to non-grayscale images (such as images in RGB or YCbCr formats), the computational complexity would increase substantially due to the need to process three color channels instead of one. Specifically, the preprocessing pipeline would need to extract residuals and bit planes from each channel (i.e., R, G, and B), resulting in a fifteen-channel tensor instead of the current five-channel input (one residual + four LSBs per channel). This would affect both memory usage and training time. The convolutional layers in the CNN would require additional parameters and operations in the first layers to handle the increased input dimensionality. Furthermore, training on color images would require larger batch sizes and longer convergence times to capture inter-channel dependencies. While feasible, this extension would multiply the cost of forward and backward passes by approximately 2.5\(\times\) to 3\(\times\), depending on the architecture depth and number of filters. Additional complexity would also arise in tuning the preprocessing logic and validating semantic consistency in more visually complex contexts. Despite this, the model's architecture is modular and could be adapted to color image inputs by adjusting the input layer and increasing the CNN capacity. Future work could explore this direction using optimized color-specific representations such as YCbCr or residual-based fusion strategies. Additionally, although the NLP component is currently configured for Spanish using the spaCy model \"es_core_news_md\", the modular nature of the system allows for seamless integration of other language models. By switching to language-specific pipelines (e.g., English, French, or Portuguese), the system can adapt to different linguistic environments. This multilingual potential extends the applicability of the proposed architecture to international forensic scenarios, provided that reliable language models and linguistic resources are available. Despite these challenges, the results suggest several promising directions for future research. An essential area is the construction and use of more datasets and realistic datasets, including color and compressed formats, and potentially real-world stego-content captured from forensic case samples. Improving the robustness of text extraction would also enhance the system's adaptability. Furthermore, integrating explainable AI techniques could enable the system to highlight or visualize regions suspected of concealing information, thereby increasing transparency in forensic analyses. Lastly, optimizing the architecture for resource-constrained environments remains a priority. Exploring lightweight CNN backbones, quantization strategies, or hybrid architectures that selectively activate the NLP module only under certain confidence thresholds could make the system more scalable and efficient.
# A Method for Degenerate Primer Design Based on Artificial Bee Colony Algorithm
## 1 Introduction
As can be seen from the above studies on degenerate primers, there are three cases addressed by research on degenerate primer design: maximum coverage degenerate primer design, that is, given a set of n strings, find a primer with length k to match as many strings as possible; minimum degeneracy degenerate primer design, that is, given a positive integer d, m and a set with n strings, find a primer with length k, make it match at least m strings and have the minimum degeneracy d; and design of minimum degeneracy primers that allow mismatches, that is, given positive integers d, e and a set with n strings, find a primer of length k that matches at least m strings, has minimum degeneracy d, and allows at most e mismatches. A finite number of mispairing experiments have little effect on the results and can also improve the amplification efficiency of primers. Most of the existing methods are suitable for one or two cases of degenerate primer design, but it is difficult to solve more complex problems. In addition, the swarm intelligence optimization algorithms [15; 16] emerging in recent years have been demonstrated to be an effective means of solving complex optimization problems. Among them, the artificial bee colony algorithm offers good performance. Its application for solving practical problems in various fields has been attracting attention [17; 18; 19; 20]. Therefore, this paper studies a solution method for degenerate primer design based on the artificial bee colony algorithm. The main contributions of this paper are as follows: (1) A method based on the artificial bee colony algorithm for solving degenerate primer design problems is proposed that can not only meet the constraints of primer design, but also allows mismatch while considering the coverage and degeneracy of primers. (2) Combining the optimization process of the artificial bee colony algorithm and the ant colony algorithm, an optimization model of the hybrid artificial bee colony algorithm is constructed. (3) Based on the idea of the ant colony algorithm, the foraging strategies of all kinds of bees are redesigned, and a search space model and pheromone matrix are constructed.
# Chinese Cabbage Changes Its Release of Volatiles to Defend against _Spodoptera litura_
## 3 Results
### Quantities of 16 Specific Compounds Released  
GC-MS analysis of the headspace volatiles produced by each treatment of Chinese cabbage detected 17 compounds (Table S1). Geranyl acetone has been compared with other categories mentioned above, and we have compared the release of the remaining 16 compounds. (Figure 4): (\(E\))-2-hexenal; (\(Z\))-3-henxen-1-ol; butane,1-isothiocyanato; 1-Butene,4-isothiocyanato; (\(Z\))-3-hexenyl acetate; limonene; ally isothioxane; linalool; nonanal; decanal benzyl isothiocyanate; tetradecane; pentadecane; hexadecane; heptadecane; octadecane. Butane,1-isothiocyanato was not detected in the _S. litura_ regurgitate treatment, and limonene was only detected from S. _litura_-infested plants. The release of nonanal was increased by _S. litura_ infestation for 24 and 48 h and tended to be increased by mechanical damage, regurgitate, and _S. litura_ infestation for 12 h. regurgitate increased the release of nonanal, pentadecane, and heptadecane but reduced the release of butane,1-isothiocyanato, 1-Butane,\(A\)-isothiocyanato, and ally isothiamate. _S. litura_ infestation increased the release of pentadecane and heptadecane, but the release of 1-Butane,\(A\)-isothiocyanato and decanal. The effect of _S. litura_ infestation varied with infestation time. _S. litura_ infestation for 12 h increased the release of (\(Z\))-3-hexen-1-ol, butane,1-isothiocyanato, (\(Z\))-3-hexenylacetate, limonene; alliyisothiocyanate, linalool, nonanal, and hexadecane, the release of these compounds was decreased; however, by _S. litura_ infestation for 24 h. The quantity of (\(E\))-2-hexenal released was lower for plants infested with _S. litura_ for 12 h than for healthy plants, and (\(E\))-2-hexenal was not detected from plants infested with _S. litura_ infested for 24 and 48 h. Butane,1-isothiocyanato was also not detected from plants infested with _S. litura_ for 24 or 48 h.  
Relationships between the Release of Classes of Volatiles and the Host Searching Behavior of M. similis and S. litura  
Linear regression was used to analyze the linear relationships between the quantities of classes of volatile emissions and the selection rate of _M. similis_ and _S. litura_. According to linear regression, the _M. similis_ selection rate was positively related to the release of terpenoids (\(p\) < 0.05, Table 1). According to linear regression, the selection rate of _S. litura_ was positively related to the release of esters but was negatively related to the release of aldehydes and ketones (\(p\) < 0.05, Table 1). Relationship between the Release of Specific Volatiles by Chinese Cabbage and the Host Searching Behavior of M. similis and S. litura  
According to linear regression, the _M. similis_ selection rate was positively related with the release of limonene, linalool, and hexadecane but was negatively related with the release of (\(E\))-2-hexenal and 1-Butene, 4-isothiocyanato (\(p\) < 0.05, Table 2).
## 4 Conclusions  
The RUL prediction of rails is crucial for the safe operation of heavy-duty trains. In order to achieve an accurate prediction of rail RUL, a method based on a CNNBiLSTM-RSA model is presented in this paper. In the suggested framework, the vibration signals are used as inputs after preprocessing, and the feature extraction capability of CNN is fully exploited. The bidirectional features of the data are captured using BiLSTM, which are combined to improve the feature extraction capability to a significant extent. Then, the RSA mechanism is used to search for important information in long sequences to improve the expressive power of the network. In particular, residual concatenation is also employed in the networks to simplify the training of the network and alleviate the overfitting problem. The effectiveness of the algorithm was verified through this study using data collected from a railway site located in China. The experimental results show that the presented CNNBiLSTM-RSA model has better performance than the traditional data-driven methods in terms of prediction accuracy.  
This study takes into account the characteristics and complexity of the rail vibration signal of heavy railways to construct CNNBILSTM-RSA which can accurately predict the rail RUL and carry out rigorous experimental verifications. The CNNBILSTM-RSA can accurately predict the RUL of rails and has been rigorously verified in the experiments. It plays an important role in promoting the intelligent fault diagnosis method. The results of this paper can be used as a basis for further exploration of new research ideas. The current railway operation and maintenance of rail replacement mostly rely on statistics using the total volume of traffic and other traditional ways. This paper provides a new approach to railway operation and maintenance and represents a step toward diversifying the process with intelligent new attempts and effective supplements.  
Although the method is very accurate for rail RUL prediction, pre-training with unsupervised data can improve the model performance and reduce the training time, which will be carried out in the future.  
G.H.: investigation, designed the overall methodology, writing--original draft, writing--reviewing and editing; L.G.: investigation, designed the overall methodology, designed and performed the experiments; Y.Z.: designed and performed the experiments; Z.W.: investigation, designed the overall methodology, writing--original draft, writing--reviewing and editing; S.Y.: designed and performed the experiments. All authors have read and agreed to the published version of the manuscript.  
This work was supported
## 3 Results and Discussion
### Change of Mechanical Property
It can be found that the original porosity of the sample was 1.48%. After the reaction, the sample dissolved, and the porosity increased to 2.1% (shown in Figure 15c). However, the results of the CT analysis depend on the scanning resolution of the samples. That might be the reason why the porosity of the sample is smaller than that in Table 2 (the original porosity was 3.01%, and the porosity after the reaction was 6.20%). An increasing trend of the sample porosity with reaction time is consistent. Figure 12: The stress–strain curve of the saturated sample under the uniaxial compressive condition of the two groups after 15 days of reaction time. Figure 13: Samples of two groups after the uniaxial compression test: (**a**) 1-1 sample of Group CO\({}_{2}\), (**b**) 1-4 sample of Group control. Figure 14: The 3D reconstructed images of the 1-1 sample before and after reaction: (**a**) the image of the sample before the reaction, (**b**) the image of the sample after the reaction. Figure 15: The porosity changes of the 1-1 samples before and after the reaction according to the CT scan results. (**a**) The porosity changes of CT slices lay by lay. (**b**) The pore distribution of the sample before the reaction. The average porosity analyzed by the CT results was 1.48%. (**c**) The pore distribution of the sample after the reaction. The average porosity analyzed by the CT results was 2.10%. (**d**) The dissolved region of the sample. The porosity analyzed by the CT results was 0.62%. The results are similar to those of other studies showing that although the carbon dioxide environment changes the microstructure of rocks, it has little effect on their mechanical properties [48]. This is because the total amount of dissolved in the whole reaction process is very small, and the sample structure does not significantly change. However, the reaction rate is very fast, and the calcium ions in the solution reach a dynamic equilibrium in a few hours (shown in Figure 8). As the reaction occurs in a limited container, the dissolution is difficult to continue because the solution that could not be timely supplemented. The results of the SEM revealed that the micropores in the sample expanded rapidly as the reaction proceeded. It can be seen from Figure 15 that the corrosion area of the sample expanded along the original pores of the sample and the pores grew bigger after the reaction. [PERSON] et al. [49] found that the mechanical properties of reef limestone are closely related to porosity. Figure 16 shows the 3D visualizations of the 1-1 sample after the uniaxial compressive test, revealing that tensile fractures occurred along the pores of the sample when the sample was broken. When the dissolved pores are large enough, the mechanical properties of reef limestones may be influenced by the reaction with acidified seawater. Therefore, the effect of acidified seawater on mechanics remains unclear and requires further study and discussion.
## 4 Challenges and Future Research Directions
### Design and Data Acquisition  
Implementing UAV-enabled ITSs requires several design and software specifications that must be considered beforehand. These specification requirements depend on several factors, including the coverage area, flight time, traffic variability and monitoring scheme, real-time communication, and data acquisition. Although the recent advancements in UAV technology are already admirable, there is still a lot of scope for development in UAV designs related to ITS applications. Various actuators and sensors, fault-tolerant controls, and software need to be installed for traffic monitoring and tracking, which requires both a high energy supply and expertise. Moreover, uninterrupted communication among UAV swarms, vehicles, and control units is crucial. However, sudden malfunctions in the device and software can interrupt the continuous functioning of the system. Using alternative energy resources and real-time optimization can be investigated to enable longer flying time and ensure uninterrupted traffic monitoring. UAV-enabled ITSs provide a valuable source of big data related to traffic. Currently, visual and tracking data are collected using GPSinformation and cameras. In the future, developing an energy-efficient, integrated image processing unit for UAVs could help reduce data processing time and energy consumption.
# Automatic False Alarm Detection Based on XAI and Reliability Analysis
###### Abstract  
Many studies attempt to apply artificial intelligence (AI) to cyber security to effectively cope with the increasing number of cyber threats. However, there is a black box problem such that it is difficult to understand the basis for AI prediction. False alarms for malware or cyberattacks can cause serious side effects. Due to this limitation, all AI predictions must be confirmed by an expert, which is a considerable obstacle to AI expansion. Compared to the increasing number of cyberattack alerts, the number of alerts that can be analyzed by experts is limited. This paper provides explainability through an interpretation of AI prediction results and a reliability analysis of AI predictions based on explainable artificial intelligence (XAI). In addition, we propose a method for screening high-quality data that can efficiently detect false predictions based on reliability indicators. Through this, even a small security team can quickly respond to false predictions. To validate the proposed method, experiments were conducted using the IDS dataset and the malware dataset. AI errors were detected better than they could be by the existing AI models, with about 262% in the IDS dataset and 127% in the malware dataset from the top 10% of analysis targets. Therefore, the ability to respond to cyberattacks can be improved using the proposed method.  
cyberattack; false alarm detection; reliability analysis; explainable artificial intelligence; shapley value +
Footnote †: journal: Applied  
Article
# Real-Time Computing Strategies for Automatic Detection of EEG Seizures in ICU
## 3 Results  
In our study, we first assessed the efficacy of seizure detection in differentiating between patients with epilepsy and those with normal EEG recordings. The k-nearest neighbours (KNN) model, with a k-value of 9, demonstrated the highest sensitivity and specificity. When considering all features, the average F1 score was 0.67. However, focusing on the most critical variables, including mean values from the left and right frontal and temporal channels and the largest local differences in the detail coefficients of various transforms, resulted in an averaged F1 score of 0.76 with a standard deviation of 0.02 (the F1 scores for ten train/test splits, the averaged F1 score, and standard deviation are shown in the Supplementary Table S1).  
The sensitivity and specificity achieved were 75% and 78.8%, respectively, as determined by the confusion matrix (Figure 3A). False positives and negatives predominantly originated from patient subgroups with brain haemorrhage (29.4%), cardiorespiratory arrest (CRA) (17.6%), epilepsy (17.6%), and cranioencephalia trauma (CET) (23.5%). In terms of symptomatology, non-convulsive seizures (NCSs) accounted for 76.5% and convulsive seizures (CSs) for 23.5%. Regarding seizure location, focal seizures (FSs) constituted 52.9% and generalised seizures (GSs) 47.1%.  
\begin{table}
\begin{tabular}{c c} \hline
**Electrode Locations** & **Feature Selection** \\ \hline Left Frontal (F3/F7) & The average frequency power over 10–20 min intervals, contingent on the duration of each seizure. \\ \hline Right Frontal (F4/F8) & Mean signal values from the right and left frontal and temporal channels. \\ \hline Left Temporal (T3/T5) & The largest peak-to-peak power differences in each channel (right and left frontal and temporal) post-application of the Hilbert transform. \\ \hline Right Temporal (T4/T6) & Peak-to-peak power differences following the empirical mode decomposition method. \\ \hline \multicolumn{3}{c}{Differences in power following the application of intrinsic mode functions 1 and 2.} \\ \hline \multicolumn{3}{c}{Peak-to-peak power variations after applying biorthogonal 1.5 and 3.3; Db 3, 4, 5, 8; and Symlets 4 and 6 transforms} \\ \hline \end{tabular}
\end{table}
Table 2: Feature selection from each electrode.