esa-sceva/satcom-chunk-collection huggingface dataset:
# Zero-Day Threat Mitigation via Deep Learning in Cloud Environments
## 6 Discussion
### Future Research Directions  
The advancement of the MVT model for zero-day malware detection represents a significant step forward in cybersecurity. However, several areas require further exploration to enhance the model's performance and practical applicability.  
Firstly, addressing the issue of computational efficiency remains crucial. Future work should focus on optimizing the transformer architecture to reduce processing time without sacrificing accuracy. Techniques such as model pruning, quantization, and the development of lightweight transformer variants could enable real-time integration in cloud environments.  
Secondly, improving the model's capability to accurately detect benign files is essential. While the use of SMOTE mitigates some class imbalance issues, future research should explore advanced data augmentation techniques tailored to benign file representation. Additionally, integrating ensemble methods might help achieve a more balanced detection performance.  
Thirdly, enhancing the interpretability of the MVT model is vital for building trust in real-world applications. Implementing explainable AI (XAI) techniques, such as attention visualization and feature attribution methods, could provide insights into how the model makes classification decisions. This would not only improve transparency but also facilitate compliance with cybersecurity regulations.  
Lastly, deploying the MVT model in dynamic, real-world cloud environments remains a challenging task. Future studies should focus on testing the model under diverse network conditions and data configurations to evaluate its robustness. Developing adaptive learning mechanisms to maintain performance despite changes in the threat landscape will be critical for practical adoption.  
In summary, the MVT model presents a promising approach for detecting zero-day threats by combining vision-based analysis and transformer architectures. While it excels at detecting malware, further improvements are required to address bias, reduce computational overhead, and enhance interpretability. Future work should focus on optimizing the model for scalable deployment and extending its capabilities toward multiclass classification in dynamic cloud environments.
_Applied_  
_sciences_  
_Acticle_  
**Locally Optimal Subsampling Strategies for Full Matrix Capture Measurements in Pipe Inspection**  
**[PERSON] 1,2,3,*, [PERSON] 1,3, [PERSON] 1,3, [PERSON] 1, [PERSON] 1\({}^{\text{\textregistered}}\) and [PERSON] 1,2**  
1 Fraunhofer Institute for Nondestructive Testing IZFP, 66123 Saarbrucken, Germany; [PERSON].K.); [PERSON][EMAIL_ADDRESS] (T.S.); [PERSON]] (F.R.); [EMAIL_ADDRESS] (A.O.)
2 HTW Saar, 66117 Saarbrucken, Germany
3 Technische Universitat Ilmenau, 98693 Ilmenau, Germany
* Correspondence: [EMAIL_ADDRESS]  
**Abstract:** In ultrasonic non-destructive testing, array and matrix transducers are being employed for applications that require in-field steerability or which benefit from a higher number of insonification angles. Having many transmit channels, on the other hand, increases the measurement time and renders the use of array transducers unfeasible for many applications. In the literature, methods for reducing the number of required channels compared to the full matrix capture scheme have been proposed. Conventionally, these are based on choosing the aperture that is as wide as possible. In this publication, we investigate a scenario from the field of pipe inspection, where cracks have to be detected in specific areas near the weld. Consequently, the width of the aperture has to be chosen according to the region of interest at hand. On the basis of ray-tracing simulations which incorporate a model of the transducer directivity and beam spread at the interface, we derive application specific measures of the energy distribution over the array configuration for given regions of interest. These are used to determine feasible subsampling schemes. For the given scenario, the validity/quality of the derived subsampling schemes are compared on the basis of reconstructions using the conventional total focusing method as well as sparsity driven-reconstructions using the Fast Iterative Shrinkage-Thresholding Algorithm. The results can be used to effectively improve the measurement time for the given application without notable loss in defect detectability.  
**Keywords:** total focusing method; compressive sensing; FISTA; full matrix capture; NDT methods; signal reconstruction; defects
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 2 The Principle and Theory of Rainfall Measurement by CML
### Mathematical Models
#### 2.2.3 Wet Antenna Attenuation (WAA)  
WAA (\(A_{WAA}\)) refers to the signal attenuation due to the water film attached to the radome during the rainfall, and it will last for a period of time until the water on the radome evaporates. Because \(A_{baseline}\) is determined prior to the rainfall event, WAA is generally not included in \(A_{baseline}\). Usually, WAA is assumed as a constant value (1-2 dB) or determined by the RSL time series of the link and its surrounding links in the previous time, and the time probability distribution function of WAA can also be estimated by data-driven algorithms.
## 5 Experimental Evaluation and Results
### Concurrency  
In a multi-threaded environment, performing multiple queries can pose challenges due to concurrent access to a hash table. This is particularly true for costly operations like full-table rehashing, which require exclusive access to the entire hash table and can potentially block subsequent operations, leading to increased response times. This challenge becomes more critical as the size of the hash table increases. To address this, we conducted an evaluation of the latency of concurrent operations, specifically, insertion and search operations.  
When running a large number of insertions in a multi-threaded environment, the insertion throughput of all the compared schemes increased. Both Dash and ESH exhibited superior overall performance in this scenario. ESH, in particular, demonstrated better performance by reducing lock contention to the single-bucket level, whereas Dash locks a segment for operations. This reduction in lock contention contributes to the improved performance of ESH in concurrent operations.  
Figure 8: Throughput Comparison for a varying number of threads.
# LEO-Augmented GNSS Based on Communication Navigation Integrated Signal
## 2 Performance of the Navigation Signal
### CN0 of the Navigation Signal  
For methods 2 and 3, the power of navigation signal is assumed to be identical to the total power of the communication signal in the same band. Therefore, the CN0 of the navigation signal is shown as follows,  
\[CN0_{max}=CN0_{total}+10\log(n_{B}/N_{B}) \tag{10}\]  
where \(CN0_{total}\) is the rate of the total power of the received signal and \(N_{0}\). It is expressed as follows,  
\[CN0_{total}=\frac{P_{c}+P_{max}}{N_{0}} \tag{11}\]  
It should be noted that if the band of navigation signal and communication signal are separated, navigation signal and communication signal will not interfere each other. Therefore, the power of the navigation signal can be increased according to the requirements for methods 2 and 3.  
For method 1, the communication signal is high power jamming for the navigation signal. Note that the communication signal is composed of many unit bands with the same power and the bandwidth of navigation signal is times of the unit band. Therefore, the communication signal can be treated as white noise and the power is the same as the power in the total band. The PSD of the effective white noise should be \(P_{c}/B\), where B is the total bandwidth. Then, the effective CN0 of navigation signal for the receiver is  
\[CN0_{eff}=\frac{P_{max}}{P_{c}/B+N_{0}} \tag{12}\]  
The following figure shows the effective CN0 of the navigation signal versus \(CN0_{total}\) under different value of \(CNSR\).  
In Figure 6, the legend means \(CNSR\) in dB and the black full line is the corresponding CN0 of GNSS signals. From Figure 6, we draw the following conclusions.  
1. When \(CN0_{total}\) is below 60 dBHz, \(CN0_{eff}\) increases almost linearly with the increment of \(CN0_{total}\). When \(CN0_{total}\) is above 70 dBHz, the effective PSD of the communication signal grows to be larger than thermal noise. Therefore, the increment rate of \(CN0_{\text{eff}}\) goes down.
2. When \(CN0_{total}\) is fixed, \(CN0_{eff}\) decreases with the increment of \(CNSR\). When \(CNSR\) is lower than 15 dB, \(CN0_{eff}\) holds advantage over the CN0 of GNSS signals for the whole range. Therefore, the value of \(CNSR\) should be lower than 15 dB.  
Figure 6: The \(CN0_{eff}\) versus \(CN0_{total}\) under different \(CNSR\).
## 3 Implementation and Results
### Envisat
#### 3.1.1 Baseline Case: Perfect Detection and No False Detection  
The baseline parameter is characterized by a perfect detection of all the maneuvers with no false maneuver detection. In the case study of Envisat, the baseline parameters were calibrated with a window span of 45 data points (approximately 45 days in time units) when a threshold of detection of 1-sigma was used. Figure 3 depicts the maneuvers detected as red circles. In this case, all the nine maneuvers within a period of three years were detected perfectly with no false detection.
## 2 Materials and Methods
### BiLSTM Network
For the cell state update:  
\[c_{t}^{f}=f_{t}^{f}c_{t-1}^{f}+i_{t}^{f}\tanh\Big{(}w_{xc}^{f}\cdot x_{t}+w_{ hc}^{f}\cdot h_{t-1}^{f}+b_{c}^{f}\Big{)} \tag{3}\]  
according to the output of the forgetting gate \(f_{t}^{f}\), the forgetting operation is performed on the cell state \(c_{t-1}^{f}\) of the previous moment, and then the output of the input gate \(i_{t}^{f}\) is weighted and summed with the current input information, processed by the tanh activation function to obtain the new positive cell state \(c_{t}^{f}\). The function maps the data between (\(-1\),\(1\)) and is used to perform a nonlinear transformation of the input information, \(w_{xc}^{f}\). The function maps the data between (\(-1\),\(1\)) and (\(-1\),\(1\)) for the nonlinear transformation of the input information. \(w_{hc}^{f}\) is the forward weight matrix between the input \(x_{t}\), the positive hidden state \(h_{t-1}^{f}\), and the updated part of the cell state at the previous moment, and \(b_{c}^{f}\) is the forward bias term. For the output gate:  
\[\sigma_{t}^{f}=\sigma\Big{(}w_{xo}^{f}\cdot x_{t}+w_{ho}^{f}\cdot h_{t-1}^{f}+ w_{co}^{f}\cdot c_{t}^{f}+b_{o}^{f}\Big{)} \tag{4}\]  
the sigmoid activation function is used to determine which parts of the forward cell state \(c_{t}^{f}\) will be output to the forward hidden state \(h_{t}^{f}\) at the current moment, \(w_{vo}^{f}\). The sigmoid activation functions \(w_{ho}^{f}\), \(w_{co}^{f}\), and \(b_{o}^{f}\) are the corresponding forward weight matrices and bias terms, respectively. And for the positive hidden status update:  
\[h_{t}^{f}=\sigma_{t}^{f}\tanh\Big{(}c_{t}^{f}\Big{)} \tag{5}\]  
the output of the output gate \(o_{t}^{f}\) is multiplied by the forward cell state \(c_{t}^{f}\) after processing by the activation function to get the forward hidden state \(h_{t}^{f}\) at the current moment. The computation of the reverse hidden state is similar to that of the forward direction, except that the computation order is reversed from the end of the sequence, and that after obtaining the direction hidden state \(h_{t}^{b}\), it is spliced with the output of the forward hidden state in the feature dimension, to obtain the final hidden state of the BiLSTM at each time step \(h_{t}^{f}=\left[h_{t}^{f},h_{t}^{b}\right]\), which is used in the subsequent computation of the output layer [12; 17]. The running schematic is shown in Figure 2:  
Network Security Situational Awareness Method Based on Improved Particle Swarm Optimization-BiLSTM Networks  
Utilizing the feature collection method described in Section 2.2, the data is split into training and testing sets at a ratio of 8:2. The model is then trained and refined to develop another model, with clear learning outcomes, which is subsequently evaluated using the testing set.
# Multi-Objective Optimization of Square Corrugation Multilayer Nested Structures
## 4 The Optimization Results of SCOD
### The Pareto Solution of Multi-Objective Optimization  
A group of Pareto solutions of multi-objective optimization with better convergence, diversity and non-constraint degree is obtained by using the DCNSGA-III algorithm. It should be noticed that \(SEA\) and \(CFE\) are benefit indicators and \(IPCF\) is a cost indicator.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Target** & \(MAX\) & \(MRE\) & \(RMSE\) & \(R^{2}\) \\ \hline \(IPCF\) & 0.03326 & 0.016487 & 0.01947 & 0.99579 \\ \(SEA\) & 0.16793 & 0.07733 & 0.08729 & 0.91303 \\ \(CFE\) & 0.17481 & 0.08694 & 0.10208 & 0.88035 \\ Tolerance & 0.3 & 0.2 & 0.2 & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Tolerance and scores of error criteria.  
In engineering practice, the structure with a larger \(SEA\) and \(CFE\) and smaller \(IPCF\) are preferentially considered. The 3D graphic of Pareto solutions corresponding to objective values is shown in Figure 11, which delivers the relationships of objective values.  
In Figure 11, the Pareto solutions obtained by the DCNSGA-III algorithm can be observed in the 3D space and the projections are also marked in planes that reflect the relationships between every two target values. As the value of \(IPCF\) increases, the \(SEA\) also increases. The changes between \(IPCF\) and \(CFE\) reflect the same trend. It is worth noting that as the \(SEA\) value increases, the \(CFE\) also increases, indicating that while reducing the cost indicator, the benefit indicator will also be sacrificed. In addition to considering the optimal solution obtained through TMDSM, the other three optimal single performance solutions generated by referencing a single target value are also marked in the figure. As shown in Figure 11, the optimal solution is consistently located in the lower left quarter in each plane. It can be expressed by the fact that the optimal solution searched by TMDSM prefers to possess smaller \(IPCF\) values and the highest possible \(SEA\) and \(CFE\) values. In addition, the other three single best performance values are all located at the extreme positions of the Pareto alternative solution set. It can be seen that the alternative solutions implement the brightest endergonic performance and highest \(IPCF\), which confirms that this optimization algorithm achieves satisfactory diversity and convergence of the population. Due to the conflicting objectives and indicators, it is necessary to balance the relationship between the three in practical engineering. The set of optimal solutions obtained by the methods used in this paper only provides some reference significance for engineers.  
Figure 11: The Pareto front in 3D space and its projection in 2D planes: (**a**) the Pareto solutions are reflected in 3D space; (**b**) the Pareto solutions are reflected in the plane of \(IPCF\)-\(SEA\); (**c**) the Pareto solutions are reflected in the plane of \(IPCF\)-\(CFE\); (**d**) the Pareto solutions are reflected in the plane of \(SEA\)-\(CFE\).
# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 3 State of the Art and Related Products
### Peer Review Literature
#### 3.1.1 Satellite-Data-Driven Agriculture
[21] uses unsupervised classification to evaluate the potential of freely accessible satellite imagery for cotton root rot detection over methods involving aerial multispectral imagery. Although Sentinel-2A missed some small rot patches, overall, it outperformed the airborne images on both field and regional levels. These findings show that images acquired through the Copernicus program may be utilized to identify cotton root rot and provide prescription maps for disease treatment at specific locations. However, most prior studies on cotton identification using remotely sensed pictures have relied heavily on training samples, which are time-consuming and expensive to obtain. To get around this restriction, ref. [22] attempts to develop a new index to identify cotton within an area of interest, termed the Cotton Mapping Index (CMI). Time series generated from S-1 SAR and S-2 MSI images were used for automatic cotton mapping and were assessed on both American and Chinese locations, achieving an accuracy of 81.20% for cotton classification. The advantage of the suggested index over traditional supervised classifiers such as Random Forests is that it requires no training samples and can obtain the map of cotton distribution before the harvest. We therefore claim that CMI calculated from Sentinel imagery can be used for accurate cotton mapping. Reliable crop yield prediction at the field level is crucial for managing difficulties and mitigating climate variability and change impacts during production. While the studies that were already presented achieved considerable results, we note that there is still a lack of accurate disaster vulnerability models that can be used to estimate yield losses and their pure insurance rate, which will ultimately assist the farmers and public sectors in planning their crops.
## 2 Materials and Methods  
### Gel and Powder Preparation  
Nanopowders of La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\) were prepared by the sol-gel method (see Figure 1), while the La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\) modified with platinum was obtained by post-synthesis impregnation.  
In the synthesis, the reaction started from lanthanum (III) nitrate hexahydrated [La(NO\({}_{3}\))\({}_{3}\)\(\times\) 6H\({}_{2}\)O] (Merck, Darmstadt, Germany) as the La precursor and titanium (IV) isopropoxide [Ti[OCH(CH\({}_{3}\))\({}_{2}\)]\({}_{4}\)] (Merck, Hohenbrunn, Germany) as the precursor of Ti. The molar ratio of La/Ti was 1:1. The lanthanum precursor was dissolved in glacial acetic acid (CH\({}_{3}\)COOH) (Sigma-Aldrich, Steinheim, Germany) in a first solution, while the titanium precursor was mixed with isopropanol (i-C\({}_{3}\)H\({}_{7}\)OH) (Merck, Hohenbrunn, Germany) in a second solution, both with a mass ratio of 1:5. The two freshly prepared solutions were mixed and stirred for 30 min. The resulting translucent solution was placed in a water bath at 60 \({}^{\circ}\)C for 60 min until a whitish powder was formed. This powder was dried at 80 \({}^{\circ}\)C (\(\sim\)3 h), then thermally treated at 900 \({}^{\circ}\)C for 2 h to obtain the white La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\) nanopowder, noted as LTA.  
The choice of the La(NO\({}_{3}\))\({}_{3}\)\(\times\) 6H\({}_{2}\)O and Ti[OCH(CH\({}_{3}\))\({}_{2}\)]\({}_{4}\) precursors for obtaining high-quality La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\) by the sol-gel method allows for good control over the stoichiometry and leads to a homogeneous distribution of La and Ti ions at the molecular level.  
Figure 1: The flowchart of the sample preparation by the sol–gel method.  
Lanthanum nitrate provides La\({}^{3+}\) ions without introducing undesired anions (such as Cl\({}^{-}\), which could remain as impurities or create by-products). Titanium alkoxides are the preferred titanium precursors in sol-gel chemistry due to their tunable reactivity. The alkoxide groups readily undergo hydrolysis and condensation, forming the Ti-O-Ti and La-O-Ti structures. Another advantage of these precursors is that they decompose cleanly upon calcination. This clean decomposition leads to fewer impurities and better control of the crystallinity and phase purity in the final La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\) powder [15; 54; 56; 60; 61].  
The metal-modified catalysts were prepared as follows: First, the previously obtained nanopowder was dispersed in distilled water. Then, an aqueous solution containing the dissolved 1 mol\(\%\) noble metal precursor, PtCl\({}_{4}\) (Fluka, Buchs, Switzerland) was slowly added to La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\) suspended previously in water and left under continuous stirring at room temperature for 24 h. The metal reduction process was completed by slowly adding 5 mL of aqueous solution with 0.04 g of freshly dissolved NaBH\({}_{4}\) (TCI, Tokyo, Japan) to the La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\) suspension. The solid material was recovered by filtration, washed with distilled water, and dried at 80 \({}^{\circ}\)C for 5 h [38].
# Enhanced Atrous Extractor and Self-Dynamic Gate Network for Superpixel Segmentation
## 4 Experiments
### Ablation Study  
To prove the effectiveness of every part of our proposed methods, we also conduct experiments on BSDS500. As shown in Figure 9, to prove the effectiveness of our proposed enhanced atrous extractor and self-dynamic gate, the baseline means we remove all our proposed parts. The enhanced atrous extractor and the only add gate stand for only adding the enhanced atrous extractor and the self-dynamic gate, respectively. We can see that if we add the enhanced atrous extractor, the ASA has a large increase, which proves the effectiveness of the enhanced atrous extractor. It stands for the multi-scale superpixel feature with contextual information that is necessary for the superpixel segmentation.  
Figure 8: The visual results of our methods and the other state-of-the-art methods.  
And we can see that if we only add the self-dynamic gate, the performance still has an increase. It means that our proposed filtered pixel and superpixel feature is good for superpixel segmentation, which proves the effectiveness of our proposed self-dynamic gate.  
To prove the superiority and influence of different parameters, we replace the enhanced atrous extractor with other feature extraction methods, such as the Vaillant transformer and AINet. The Vaillant transformer is the basic classic transformer without any additional modifications. The transformer of AINet can extract the superpixel feature explicitly. As shown in Figure 9(a), we can see that our enhanced atrous extractor achieves the best performance. Compared with the Vaillant transformer and the transformer of AINet, we can see that our feature without contextual information results in performance degradation. And we replace the self-dynamic gate with the other feature fusion module. As shown in Figure 9(c), we can see that our proposed self-dynamic gate achieves the best performance, which proves the superiority of the self-dynamic gate. And we can see if we introduce the add and multiply to fuse the feature that the performance has a large decrease, which means that our self-dynamic gate can fuse the feature better. We replace the activation function of our proposed self-dynamic gate to probe the effectiveness of different combinations of activation. As shown in Figure 9(b), we can see that if we replace the sigmoid with Tanh or ReLU, the performance is similar to ours. But if we replace the sigmoid with Softmax, the performance has a large decrease, which means Softmax is harmful to the filtering features.