esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Literature Review
The authors created a model using both MDA and logistic regression. However, many studies related to prediction models from Hungarian authors are in their national language, so they are difficult to use for an international reader. As is visible, financial distress prediction models have historically been widely used to predict the financial distress of firms. In recent years, the development of prediction models has gained further attention due to the global economic shocks triggered by the COVID-19 pandemic. The pandemic significantly impacted global economies, leading to widespread financial distress across numerous sectors. Several studies have analysed the performance and adaptations of existing prediction models during the pandemic. However, several authors found that the pandemic revealed several limitations in using traditional financial distress prediction models and the need for more dynamic models capable of incorporating sudden economic shocks. For instance, [PERSON] and [PERSON] [38] conducted a re-estimation of traditional models, finding that the original Altman Z-score required significant adjustments to remain accurate during the pandemic. [PERSON] [39] conducted a comparative analysis of service companies during the pandemic period and found that traditional models such as the Springate and Altman Z-scores underperformed, as they failed to account for non-financial variables that became critical during the pandemic. [PERSON] and [PERSON] [40] conducted a study on predicting bankruptcy risks in distressed insurance companies in Oman during the pandemic period. The authors used the Altman Z-score model to assess financial distress in both pre- and post-pandemic periods (2019-2020). The study revealed that while traditional financial models, such as the Z-score, could predict financial distress to some extent, they were less effective when used in the volatile insurance sector, mainly due to external factors such as market instability and government intervention during the pandemic. The authors recommended incorporating real-time data and non-financial factors to improve model accuracy. Similarly, [PERSON] and [PERSON] [41] have called for the inclusion of external factors like market conditions, government interventions, and non-financial metrics. [PERSON] et al. [42] assessed the bankruptcy risk in Bulgarian agricultural enterprises using several classical models, including the Altman, Springate, and Fulmer models. The study highlighted that while these models provided accurate predictions before the pandemic (2019), they required adjustments during the pandemic period. The research suggests that sector-specific factors must be integrated to improve prediction accuracy. Similarly, [PERSON] et al. [43] analysed the banking sector in Indonesia and found that model accuracy during the pandemic was significantly reduced, emphasising the need for real-time data and adaptive algorithms.
# A Magnetotelluric Signal Acquisition and Monitoring System Based on a Cloud Platform
## 6 Results
### Consistency Testing  
Consistency testing of this acquisition control system was conducted at the PetroChina base in Bazhou, Hebei, with the Phoenix MTU-5A as the reference instrument. The observation method used was two electric channels and two magnetic channels, with an electric dipole spacing of 20 m. The acquisition frequency range was 0.01 Hz to 1000 Hz, and the acquisition duration was eight hours. The test site was chosen by PetroChina (shown in Figure 22), which is a commonly used test site near the base. The same electrodes and magnetic rods were used, and the test was conducted on two consecutive days.  
The test results are shown in Figure 23. The two acquisitions showed good consistency below 0.7 Hz, while there were fluctuations above 0.7 Hz, with slight differences at different frequency points, likely due to different interferences during the two time periods. The curve shapes and jump points were consistent with the differences between two acquisitions using the same device. Table 3 shows the error parameters of the test, which are basically consistent between the two instruments. Using the same electrodes and magnetic rods, the curve shapes of the two instruments' acquisitions were consistent, indicating good consistency between the two instruments, and the instrument differences did not cause changes in the resistivity and phase curves.  
\begin{table}
\begin{tabular}{c c c c} \hline
**CPU** & **Internal Storage** & **Peak Bandwidth** & **Operating System** \\ \hline Two-core & 4 GiB & 5 Mbit/s & CentOS 8.2 64 bit \\ \hline \end{tabular}
\end{table}
Table 2: Cloud platform server test results.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Concurrency** & **Average Delay (ms)** & **Average RPS** & **Success Rate** \\ \hline
20 & 7.1 & 2572.25 & 99.99\% \\
100 & 31.62 & 2813.21 & 99.64\% \\
200 & 65.89 & 2715.02 & 99.11\% \\
500 & 174.56 & 2614.70 & 97.97\% \\ \hline \end{tabular}
\end{table}
Table 1: Cloud platform server parameters.  
Figure 22: Consistency test site in Bazhou, Hebei (**a**) (red circle on the right indicates the four base areas (**b**)).
# Adaptive Bat Algorithm Optimization Strategy for Observation Matrix
## 4 Experimental Simulation
### Signal Reconstruction Experiment
#### 4.2.2 Signal Reconstruction Experiment and Analysis
It can be seen from Figure 5 that the reconstruction error of Gaussian, Bernoulli, and SVD-M fluctuates greatly and the stability of reconstruction error is poor. Conversely, BA and ABA have better reconstruction error values and the variation is stable. Figure 5: Reconstruction error of each algorithm to signal-2 with different compression ratio: (**a**) Rate = 0.3; (**b**) Rate = 0.35; (**c**) Rate = 0.4; (**d**) Rate = 0.45; (**e**) Rate = 0.5. Figure 6: _Cont._  
Figure 6 shows the reconstructed error values obtained for the signals \(S_{3}\) under each observation matrix and algorithm. It can be seen from Figure 6 the poor reconstruction error and the larger error value for Gaussian, Bernoulli, and SVD-M. For BA and ABA, the reconstruction effects are significantly better than the observation matrices. Figure 7: _Cont._  
Figure 7 illustrates the reconstruction error plots for several observation matrices and algorithms for the \(S_{4}\) signals. Comparing the BA and ABA, the reconstruction error values and stability of Gaussian, Bernoulli, and SVD-M are poor, which also shows that the reconstruction effects of BA and ABA are obviously better from Figure 7. Moreover, it can be seen from the above Figures 4-7 that the ABA has better stability and error value than the BA algorithm.
# RoPT: Route-Planning Model with Transformer
## 5 Experiments
### Baselines  
To better evaluate the performance of RoPT, the following baselines are chosen for comparison:  
Dijkstra: A traditional method for finding the shortest path from the source node to the destination node, where the weight of an edge corresponds to the time consumption of the edge in the road network.  
Heuristic-A* [32]: In this method, when computing the edge cost, traffic and weather conditions are considered in the penalty term of the edge cost in A*. Due to the fact that weather condition is not considered in other baselines, only traffic congestion is considered in our simulated method.  
CSSRNN [1]: In this method, route planning is carried out with the spatial correlations of nodes in the road network extracted based on the node reachable matrix, and sequence correlations in the trajectory extracted based on the RNN.  
GETNext [3]: In this method, a global flow graph based on trajectories is constructed. Firstly, the potential representations of nodes are learned with the GCN, then the embedded information, such as the movement mode, user, node, and time, are combined and fed into the Transformer to predict the next node to be visited. Due to the fact that this method is onlydesigned for predicting the next POI, in order to improve its performance in route planning, destination information is added into the Transformer in the following experiments. The method of adding the destination is similar to RoPT: destination embedding learned from the GCN is added to the end of the current route.  
GetNext-N is a variant of GetNext that considers only neighboring nodes when calculating the transition probability.  
NEUROMLR [2]: In this method, a GCN and Lipschitz embedding are used in order to learn the potential representations of nodes. To reduce the dimension of the temporal features of a node, which is formed with concatenating the potential representation of the node in all time slices, Principal Component Analysis (PCA) is introduced. Finally, the transition probability can be predicted on representations of the node to be transferred, the current node, and the destination node.  
Graphormer [6]: This method incorporates the structure of a graph into the Transformer via three encoding techniques. Centrality encoding adds learnable embeddings based on the node degree to the initial features. Spatial encoding uses the shortest path distance (SPD) to model the spatial relationships between nodes. Edge encoding adjusts attention weights by averaging the dot products of edge features and learnable embeddings along the shortest paths. These encodings allow Graphormer to capture both local and global graph dependencies, achieving strong performance across graph representation tasks.
# Enhancement of Ultrasonic Guided Wave Signals Using a Split-Spectrum Processing Method
## 3 Signal Analysis  
This section shows how this methodology has been evaluated. Initially, signals have been simulated using the technique that was presented by [PERSON] [7] to represent the guided wave reflections in a pipe with known dispersion characteristics. This allows the SSP limitation to be found under controlled conditions. Secondly, experimental data was collected from an eight inch pipe that was equivalent to the simulated pipe. This gives two benefits: (i) it allows the approach to be tested under more realistic conditions; and (ii) the reusable SSP parameters across equivalent pipes could be evaluated.
# A Polygonal Model to Design and Fabricate Ion-Exchanged Diffraction Gratings
## 4 Design and Fabrication of a Grating with Suppressed Zero Order and a Three Splitter  
Now, we show the practical utility of the model introduced in Section 2 by designing and fabricating two diffractive elements: a grating that suppresses its zero order and a three splitter. The procedure that we used for this was the same in both cases. First, we calculated the ion-exchange time necessary to obtain each element through Equations (7) and (8), as well as the characterization presented in Section 3 (Figures 4 and 5). Next, we fabricated such an element following the same fabrication process explained in Section 3, but using the previous calculated time. Finally, we measured the diffraction efficiencies for \(\lambda_{0}\) = 632.8 nm to check the quality of the element. Along these measurements, we also checked that these gratings had negligible absorption losses at this wavelength and no sensitivity to polarization of the beam. Moreover, we quantified their noise by measuring the stray light between 0-1 orders and 1-2 orders. We obtained a signal-to-noise ratio higher than 35 dB. The experimental setup to measure the diffraction efficiency of these grating elements is shown in Figure 6, and a close up photo of one of the fabricated gratings can be seen in Figure 7.  
Of course, the design of these elements was restricted to the parameters of the gratings used to characterize the ion-exchange process, that is a period \(\Lambda\) = 30 \(\upmu\)m and an opening width \(w\) = 10 \(\upmu\)m. However, other periods and openings could be used provided that a previous characterization is done. In particular, by using this polygonal model, the period and the opening of the gratings could be optimized for a specific element on the basis of, for instance, a large fabrication tolerance or another merit function. On the other hand, this model provides the minimum period that can be achieved using this technique of  
Figure 5: Plot of the efficiencies of the orders 0 (blue line), \(\pm\)1 (red line), \(\pm\)2 (green line), and \(\pm\)3 (magenta line) with the square root of the ion-exchange time according to Equations (7) and (8) and Figure 4. The experimental efficiencies of the five gratings used in this work are also plotted.  
grating fabrication. From Figures 1 and 2, it is clear that the limit is found for \(v=0\), that is \(\Lambda_{min}=w+2s\). The particular value will depend on \(s\), which will be determined by the particular element to be fabricated. For example, for a grating with suppressed zero order (see Section 4.1), \(\Lambda_{min}\approx 15\) um, and for a three splitter (see Section 4.2), \(\Lambda_{min}\approx 9\) um. In both cases, an opening width of \(w\approx 1\) um is assumed.
## 4 Results and Discussion  
### Void Ratio with Change in Vertical Effective Stress  
Figure 3 shows the compressional behavior of the specimens under one-dimensional loads. At a vertical effective stress of 12.5 kPa, all the A-B mixed specimens started from a void ratio of approximately 0.608 and presented a similarly decreasing void ratio with increasing load (although the mixed specimens including 90% or 100% of fine particles B were less compressed than the others (Figure 3a)). For A-C mixed specimens, the specimens with 20% and 40% of fine particles C started from a low initial void ratio (\(e\) = 0.600-0.604) and showed significant decreases in the void ratio, whereas the others showed a higher initial void ratio and lower compression (Figure 3b).  
The theoretical pore body size of a simple cubic packing of spherical particles equals 0.414\(D\), which can be considered the loosest packing (Figure 4a). In addition, the pore body size of the cubic tetrahedral packing of spherical particles equals 0.155\(D\), which can be theoretically considered the densest packing (in Figure 4b, note that \(D\) indicates the diameter of the largest particles) [24]. Silica sand, with a void ratio of \(e\) = 0.49-0.79, has been considered a material with a loose density [25]. Given the initial void ratio and particle size of the baseline specimen A (i.e., 0% B or 0% C), \(e\) = 0.608, and \(D_{50}\) = 0.84 mm, respectively, its theoretically expected maximum pore body size is approximately 0.348 mm, which is obtained from 0.414\(D\). Considering the particle size distribution of fine particles B and C in Figure 1, they have \(D_{50}\) values of 0.33 and 0.16 mm, respectively. Fine particles C may easily fill the pore spaces in sample A. As such, the large particles of sample A form the structural skeleton, and the fine particles C in a range of 20% to 40% may be appropriate amount to fill the pore body constructed by sample A and therefore decrease the void ratio of mixed specimens (see Figure 3b). In addition, a change in compressional load increases both the coordination number and the contact area of the particles, thereby decreasing the porosity (or void ratio), which has been consistently reported in the literature [26; 27; 28].  
Figure 3: Void ratio with respect to one-dimensional load.
## 2 Mathematical Formulation of Object Localization and Tracking Algorithms
### TOF Estimation
#### 2.3.1 The Envelope Model of Ultrasonic Echo Signal  
In general, the GN form of the ultrasonic envelope can be modeled as an analytical expression [32], such that:  
\[h_{g}(\mathbf{z_{g}},t)=h_{g}(t_{0},a_{g},\alpha,p,t)=a_{g}\Big{(}\frac{t-t_{ 0}}{p}\Big{)}^{\alpha}e^{\left(\frac{t-t_{0}}{p}\right)} \tag{7}\]  
where \(\mathbf{z_{g}}=[t_{0},a_{g},\alpha,p]\), \(t_{0}\) is the TOF, \(a_{g}\) accounts for the amplitude of the ultrasonic echo signal, \(\alpha\) and \(p\) are shape parameters of the specific ultrasonic transducer, and \(t\) is time. The model uses the parabolic term \((\frac{t-t_{0}}{p})^{\alpha}\) and the exponential decay term \(e^{(\frac{t-t_{0}}{p})}\) to characterize the envelope waveform.  
The other model used in this study is the DE model [33] such that:  
\[h_{d}(\mathbf{z_{d}},t)=h_{d}(t_{0},a_{d},\beta,\gamma,t)=a_{d}\big{(}e^{- \beta(t-t_{0})}-e^{-\gamma(t-t_{0})}\big{)} \tag{8}\]  
where \(\mathbf{z_{d}}=[t_{0},a_{d},\beta,\gamma]\), \(t_{0}\) is the TOF, \(a_{d}\) accounts for the amplitude of the ultrasonic echo signal, \(\beta\) and \(\gamma\) are the decay factors (it is noted that \(\gamma\) must be larger than \(\beta\)), and \(t\) is time. The DE model characterizes the envelope by two exponential terms.  
It is noted that the main difference between the GN model and the DE model is that they characterize the rising edge (the reign from TOF to peak) of the echo in different methods. The GN model characterizes the rising edge as a parabolic term, while the DE model characterizes the rising edge as a concave downward curve term. These two different models are applied in the proposed system to test which one is more suitable to cooperate with other algorithms in this study, and the comparison results are shown in Sections 3 and 4.
# An Efficient Resource Scheduling Strategy for V2X Microservice Deployment in Edge Servers
## 5 Discussion  
In this article, we study the resource scheduling in containerized microservice deployment. To model this problem, we analyze its architecture and quantify the key factors in the scheduling process. After obtaining a multi-objective model, we develop the multiple fitness genetic algorithm, which introduces a container dynamic migration strategy into the process.  
To evaluate the efficiency of our model and MGFA, we use Container-CloudSim to simulate the different scenarios in V2X microservice deployment. During the simulation experiments, we find that MGFA performs better than the RR algorithm, the MF algorithm, and the FCFS algorithm, especially when we compare results in terms of the number of hosts occupied, the utilization rate of the data center, resource utilization imbalance, and calling distance.  
The method above improves the calculation efficiency of MGFA and makes it more suitable for solving microservice resource scheduling problems. However, we did not consider the situation of deploying the vehicle network application of collaboration with the central cloud. At the same time, the weights in our algorithm are decided by the administrator, which may cause faults in the scheduling process. Last but not least, we only simulate some scenarios in Container-CloudSim, and we do not evaluate our algorithm in an actual situation.  
We will develop a weight adaptive adjustment system to change weights based on the different characteristics in edge computing architecture in future work. Moreover, we will design a cloud-side collaborative microservice scheduling strategy to better suit the actual situation. Finally, we will deploy our developed system in a real V2X network to test its performance.  
Conceptualization, Y.S. and L.L.; funding acquisition, Y.S.; investigation, Y.G.; project administration, Y.S.; resources, K.Z.; software, K.Z.; writing--original draft, Y.G.; writing--review and editing, L.L. All authors have read and agreed to the published version of the manuscript.  
This work was funded by China National Key Research and Development Program (NO. 2018 YFE0197700).  
The authors declare no conflict of interest.
## 4 Discussion
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Research Questions** & **Key Findings** \\ \hline RQ1: What are the exploitation techniques for IoT vulnerabilities in air-gapped networks? & See Table 2 \\ \hline \multirow{4}{*}{RQ2: What best practices prevent IoT exploitations in air-gapped airways?} & (1) Changing the default camera password is the paramount rule in camera security. (2) Keep the cameras invisible by plugging them through the NVR’s POE (Power over Ethernet) ports to use the physically isolated NVR’s subnet from the network computer in the air gap. (3) If the air gap cannot be fully physically separated from the external network, in the case of corporations and large businesses, then a Virtual LAN is required to subnet the cameras. (4) Ensure that linked peripherals are kept to the bare minimum. External gear like printers, USB drives, and CD drives should not be connected to the system since hackers might use them as entry points. It is possible to restrict USB ports, which are frequently the most likely source of infection, using inexpensive devices like USB port locks. (5) Encrypt the air-gapped systems’ data, given their sensitivity. This does not stop data breaches or assaults but can prevent attackers from using the information if exploitation is successful. (1) Legacy anti-virus programs frequently require signature updates for newly identified threats. Some allegedly next-generation technologies heavily rely on their capacity to transmit telemetry to the cloud and process it remotely. Both will fail if the principal security posture does not require internet connectivity. Anon-device behavioral AI that is capable of autonomously detecting, guarding against, and resolving malware, ransomware, and device-based attacks from peripherals like USB drives is the solution to these issues, using the behavioral analysis of anomalies rather than signatures or file identities, detecting both well-known and new malware without the need for internet connectivity. (2) Install next-generation assessment tools that can identify underlying OEM beneath a white-labeled IP camera, thus addressing a significant gap in preventing attacks against IP cameras. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A summary of the key findings from the research questions RQ1-RQ3. audits, updates, and adherence to security protocols are key to maintaining the integrity and resilience of air-gapped systems in the face of evolving exploitation techniques.
# An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions
## 3 Cloud Security Frameworks
### NIST SP 800-144
#### 3.2.1 NIST's Advantages in Cloud Infrastructure Security  
1. Comprehensive and Proven: In the cybersecurity field, the NIST framework is well-recognized and accepted. It provides a complete and organized approach to cloud security, including risk assessment, security controls, incident response, and continuous monitoring. Based on industry input and evolving security concerns, the framework is constantly updated and optimized.
2. Flexibility and adaptability: The NIST framework is intended to be adaptable to various organizations and cloud settings. It allows organizations to adjust security controls and to their individual demands and regulatory requirements. This adaptability enables organizations to build security measures tailored to their own cloud architecture and business activities.
3. Vendor-neutral perspective: NIST SP 800-144 is vendor-neutral, concentrating on the basic security and privacy principles that organizations should consider while adopting public cloud services. This enables organizations to use the recommendations independently of the cloud service provider they select, increasing flexibility and adaptability.
4. Practical advice: The paper provides practical advice and the best for safeguarding public cloud systems. It offers practical advice on setting security controls, analyzing cloud service provider's security capabilities, and meeting compliance requirements. This assists organizations in translating advice into actionable steps to improve cloud security.