esa-sceva/satcom-chunk-collection huggingface dataset:
# Early Ventricular Fibrillation Prediction Based on Topological Data Analysis of ECG Signal
## 4 Discussion
### Evaluation of TDA Features  
To evaluate the performance of the TDA features, we ranked the entire 18 features using five features selection methods in this work. As shown in Table 6, it can be seen that the TDA features achieve high rankings under various selection methods. In Figure 7, the first seven average rankings are all from TDA features, which indicates that TDA features have an obvious advantage over both HRV features and box-counting features. This may be a result of the fact that the TDA method is able to capture potential details in the time sequence and, together with PSR, can derive the information embedded in the system corresponding to the original signal. For the box-counting features, which are also based on PSR, the characterization ability is limited because of the insufficient reconstruction dimension. Moreover, the HRV features only analyze the information contained in the heartbeat itself, excluding the information of the system contained in the original signal, causing it to get ranked at lower positions. However, the HRV feature _skewness_ achieved the highest ranking behind the TDA features, probably owing to its ability to represent changes in heartbeat rhythm patterns before the onset of VF. In addition, as shown in Table 5, the TDA features achieved the best classification accuracy, which also proves the advantage of the proposed features.
## 7 Performance Evaluation
### Use Cases and Simulated Fog Topologies  
The reported tests of the _DeepFogSim_ package refer to the Fog topologies sketched in Figure 8, hereinafter referred to as \(T1\) and \(T2\), respectively. Specifically:  
* topology \(T1\) is composed of \(Q=9\) nodes, which are arranged onto \(M=3\) tiers. Each node at \(tier\,\#m\) is connected to every node at \(tier\,\#(m+1)\); hence, the \(T1\) topology embraces 10 transmission links;
* topology \(T2\) is composed of \(Q=15\) nodes, which are arranged onto \(M=4\) tiers. Each node at \(tier\,\#m\) is connected to every node at \(tier\,\#(m+1)\); hence, the resulting \(T2\) topology embraces 42 transmission links.  
Due to space constraints, we have decided to limit to report the performance results under \(T1\) and \(T2\) topologies. However, the archived set-ups in the _DeepFogSim_ package cover several topologies, with different number of tiers and per-tier nodes.  
In the carried out simulations, we refer to a CDNN with \(L=9\) layers to be suitably scattered over the available \(M\) tiers of the Fog platform of Figure 1b. In particular, we assume the following default numerical settings:  
* \(\widehat{cm}^{(0)}=[0.045,\,0.1,\,0.3,\,0.5,\,0.71,\,0.7,\,0.9,\,0.9]^{T}\) for the compression vector;
* \(\widehat{L2T}^{(0)}_{map}=[4,\,2,\,1]^{T}\) and \(\widehat{L2T}^{(0)}_{map}=[2,\,2,\,2]^{T}\) for the mapping vectors under the two considered Fog topologies \(T1\) and \(T2\), respectively; and
* \(\widehat{T}^{(0)}_{EXIT}=[T_{S},\,T_{S},\,0.4T_{S}]^{T}\) (s) and \(\widehat{T}^{(0)}_{EXIT}=[T_{S},\,T_{S},\,T_{S},\,0.4T_{S}]^{T}\) (s) for the per-tier maximum allowed inference times under the two considered topologies in Figure 8.  
In addition, in the carried out simulations, we set the maximum allowable resources to: \(f^{(MAX)}_{jm}=9\) (Mbit/s), \(\tilde{f}^{(MAX)}_{jm}=8\) (Mbit/s), \(R^{(MAX)}_{1}=8\) (Mbit/s), and \(R^{(MAX)}_{2}=9\) (Mbit/s) under topology \(T1\) and to: \(f^{(MAX)}_{jm}=4.2\) (Mbit/s), \(\tilde{f}^{(MAX)}_{jm}=3.73\) (Mbit/s), and \(R^{(MAX)}_{in}=1.95\) (Mbit/s) under topology \(T2\). These values are the same for every node, i.e., for all \(j=1,\ldots,M\) and \(m=1,\ldots,m_{m}\).
# MSSP: A Blockchain Sharding Protocol Based on Multi-Shard Storage
## 6 Experiment
### Security  
Figure 10 illustrates a comparison of transaction throughput between MSSP and traditional sharding in the presence of different proportions of malicious nodes. The data in the figure indicate that even with the existence of malicious nodes, the transaction throughput of MSSP is still significantly higher than the other four sharding schemes. This result demonstrates that MSSP is effective in resisting the impact of malicious nodes and maintaining high performance and stability in the system.  
To meet the security requirements mentioned in Section 6.1, the failure probability must be below the threshold of \(2^{-15}\), as shown by the green dashed line in Figure 11.  
Figure 8: Comparison of confirmation latency between MSSP and four other sharding protocols at different numbers of shards.  
Analysis of the figure reveals that to satisfy a failure probability below \(2^{-15}\), the percentage of malicious nodes must be less than 14% for a multi-shard storage system to be considered secure. Additionally, as shown in the graph, with an increase in the proportion of malicious nodes, the failure rate of the system also increases, and the transaction throughput of MSSP decreases approximately proportionally. This situation occurs because within the safe threshold range, although malicious nodes cannot tamper with data, they may waste consensus time when acting as leaders. In conclusion, MSSP can ensure timely identification and prevention of blocks submitted by malicious nodes, thereby maintaining the overall security and efficiency of the system.  
Figure 11: Transaction throughput and failure probability under different malicious node percentages in MSSP.  
Figure 9: The storage overhead per node after processing 500,000 transactions in multi-shard storage with different scalability parameters \(\omega\).
# Micro-Satellite Systems Design, Integration, and Flight
## 1 Introduction  
The development of a nanosatellite as a carrier for a fleet of light sail-propelled picostatellites (ChipSats) is depicted in Figure 1.  
Satellite development has historically been a complex and expensive venture, accessible only to government agencies and large industry at a high financial and technolog  
Figure 1: (a) Moment of inertia testing for CubeSat attitude control system development; (b) balloon test to validate ChipSat long-range radio frequency capabilities.  
ical barrier of entry. NASA's space shuttle had a cost of about USD 1.5 billion to launch 27,500 kilograms into low-earth orbit, or USD 54,500 per kilogram [1]. Meanwhile, commercial launches have reduced the cost of low-earth orbit twenty-folds. There is an increasing urgency to expand horizons in space, not only for scientific knowledge and exploration but also for interplanetary travel and access to natural resources.
# Backstepping Adaptive Neural Network Control for Electric Braking Systems of Aircrafts
## 3 Control Strategy
### HIL Experimental Results  
In practical work, the braking pressure signal is more irregular which requires higher control performance of the EMA. To verify the control effects of the proposed EMA controller in an actual working environment of the aircraft, an aircraft electric braking system HIL test bench was built as shown in Figure 4b. The aircraft landing model and the antilock controller were established by MATLAB-Simulink and downloaded to the target board. Then, the reference pressure signal was output to the EMA controller and the corresponding braking pressure is realized by EMA. The main structure of the HIL test bench was analyzed in research [14] and no more detail is provided here. The control target of the HIL experiment is to control the slip ratio around the optimal value to maintain the maximum adhesive coefficient between the aircraft wheel and the runway. The slip ratio \(\lambda\) and adhesive coefficient \(\mu\) are defined as:  
\[\lambda=(V_{x}-V_{w})/V_{x}, \tag{54}\]  
\[\mu(\lambda)=c_{1}(1-e^{-c_{2}\lambda})-c_{3}\lambda, \tag{55}\]  
where \(V_{x}\) is the aircraft speed,\(V_{w}\) is the wheel speed, and \(c_{1}\), \(c_{2}\), \(c_{3}\) are constants.  
The initial conditions of the aircraft speed and the wheel speed are \(V_{x}=72\) m/s and \(V_{w}=72\) m/s, respectively. The limitation of the maximum braking pressure in the EMA controller is set at 225 kg. In the u-y function, the optimal wheel slip ratio is \(\lambda=0.16\) and the maximum adhesive coefficient is \(\mu=0.75\). All of the experimental data are saved by the upper computer and drawn by MATLAB.  
Figure 6: Braking pressure performance comparison. (**a**) Proposed method with a 3 Hz sine wave; (**b**) PID control with a 3 Hz sine wave; (**c**) Proposed method with a 2 Hz square wave; (**d**) PID control with a 2 Hz square wave.  
The HIL experimental results are shown in Figure 7a-d. It can be clearly observed that the aircraft speed and wheel speed descend steadily under the action of the braking pressure. After a short time transient, the optimal slip ratio is well tracked and the corresponding adhesive coefficient remains stable around its maximum value without significant overshoots, which means that the proposed EMA controller can meet the requirements of the aircraft braking system.
# Security Enhancement for Data Migration in the Cloud
## 2 Background Preliminaries
### Cryptographic Algorithms
#### 2.1.2 Asymmetric-Key Algorithm or Public-Key Encryption  
The asymmetric-key algorithm or public-key encryption utilizes a pair of keys--public key and private key. The public key can be revealed, but, to protect the data, the private key must be concealed. Additionally, encryption and decryption of the data must be done by the associated private and public keys. For instance, data encrypted by the private key must be decrypted by the public key and vice versa. Some of the common examples of this algorithm are RSA (Rivest-Shamir-Adleman), Diffie-Hellman key exchange, and elliptic curve techniques.
# A Method to Reduce Eddy Current Loss of Underwater Wireless Power Transmission by Current Control
## 4 Electromagnetic Field Simulation Analysis and Discussion  
In Sections 2 and 3, we analyzed the circuit model in air and seawater, and proposed two methods to reduce the eddy current loss in the underwater WPT system. In this section, we take advantage of FEA software in order to obtain the electric field intensity distribution and energy loss density distribution, and we analyze the improvement of the energy transmission efficiency of the system using two methods. For this reason, we chose to use a planar dual-coil power transmission system with symmetric distribution and establish a model as shown in the Figure 9 below, and the value of parameters in simulation are shown in Table 1.  
Figure 9: Simulation geometric models for underwater WPT system.  
In the simulation analysis and research below this section, under the condition of ensuring that the circuit compensation topology remains unchanged, we change the parameters of compensation components to make the power on the equivalent load resistance the definite value, and we analyze the energy transmission efficiency from primary-side AC to secondary-side AC so as to evaluate the effect of the two methods on reducing eddy current loss.
# Advanced Air Mobility: Opportunities and Challenges Deploying eVTOLs for Air Ambulance Service
## 4 Analysis and Findings
### eVTOL and Hybrid Aircraft Dispatch Reliability and Market Potential
Therefore, battery recharging times would need to be reduced by a factor of four for eVTOLs (to 50 min) to reduce eVTOLs total call time to 125 min (Figure 11). The reduction in total call time coupled with similar dispatch reliability and cost per ambulance trip is required for eVTOLs to effectively compete with rotorcraft in the aeromedical market. Theoretically, while eVTOLs with battery swapping capabilities and hybrid aircraft could serve this market, innovations that reduce charge times and/or simplify the process of swapping batteries are needed for eVTOLs to effectively provide air ambulance service. However, swapping heavy batteries would require specialized equipment and training (e.g., industrial battery handling and lifting accessories).
## 5 Adaptive Decision Threshold Design Method Based on Auxiliary Channel
### Adaptive Threshold Design  
The normalization threshold \(\lambda\) and the acquisition threshold \(V_{th}\) are defined by (6) and (7) in Section 3. The normalization threshold does not vary with the SNR, while the acquisition threshold is the decision threshold of the test statistic \(Z\). The relationship between \(\lambda\) and \(V_{th}\) is \(\lambda=V_{th}/P_{n}\).  
The expression of \(P_{acq}\) in relation to \(\lambda\) can be obtained from (11), and the relationship between \(\lambda\) and \(P_{acq}\) when other variables are fixed is shown in Figure 11. If \(P_{acq}(\lambda)>\gamma\), the target requirement is met. Therefore, the minimum and maximum \(\lambda\) are obtained by \(P_{acq}(\lambda)=\gamma\). The first solution of the equation is \(\lambda_{\text{min}}\), and the second solution is \(\lambda_{max}\), \(\lambda_{\text{min}}\) and \(\lambda_{max}\) are related to the incoherent accumulation length \(L\), the number of decisions \(M\), the symbol signal-to-noise ratio \(\mu\) and the target acquisition probability \(\gamma\), that is, \(\lambda_{min}=f(L,M,\mu,\gamma)\), \(\lambda_{max}=g(L,M,\mu,\gamma)\). We can use \(\gamma\) to constrain the threshold, obtain the maximum and minimum \(\lambda\) by solving the equation, and select a suitable intermediate value as the \(\lambda\). When the \(P_{acq}\) is maximum, \(\lambda\) is \(\lambda_{opt}\), which is only related to \(L\), \(M\) and \(\mu\).  
Figure 10: Acquisition algorithm based on adaptive threshold decision.  
If we choose \(\lambda_{opt}\) at the maximum \(P_{acq}\), we need to ensure that the selected \(V_{th}\) is still within the acceptable range after being affected by the noise estimation error. Because \(V_{th}=\lambda P_{n}\), the impact of noise estimation error \(\Delta\hat{P}_{n}\) on \(V_{th}\) at this time (that is, the acquisition threshold estimation error) is \(\Delta V=\Delta\hat{P}_{n}\times\lambda_{opt}\). When the error is within the required range of \(P_{acq}\), its relationship with \(V_{\min}\) and \(V_{\max}\) is as shown in Figure 12.  
\(V_{th}\) is related to \(L\), \(M\), \(\mu\) and \(P_{n}\), so we still need to analyze \(\mu\) and \(P_{n}\).
# Three-Dimensional Real-Scene-Enhanced GNSS/Intelligent Vision Surface Deformation Monitoring System
## 3 Data Sources  
The input data of this framework can be grouped into multi-source sensing data and 3D data.
# A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose
## 2 Theory and Methodology
### Selection of Sensor Evaluation Indicators
#### 2.1.3 Correlation  
Correlation can reflect the relationship between the responses of different sensors to the same target gas. The raw data collected by the E-nose often have high levels of redundancy, and the strong correlation between sensors is one of the most important influencing factors. Its calculation equation is as follows:  
\[R_{xy}=\left|\frac{\sum_{i=1}^{N}(x_{i}-\overline{x})(y_{i}-\overline{y})}{ \sqrt{\sum_{i=1}^{N}(x_{i}-\overline{x})^{2}(y_{i}-\overline{y})^{2}}}\right| \tag{3}\]  
where \(x_{i}\) and \(y_{i}\) are the \(i^{th}\) measurement values; \(\overline{x}\) and \(\overline{y}\) are the mean values of the data collected from sensors \(x\) and \(y\), respectively; and \(R_{xy}\) is the absolute value of the correlation coefficient between the two sensors and varies between 0 and 1. The closer \(R_{xy}\) is to 1, the stronger the correlation between variables \(x\) and \(y\), while on the contrary, the closer \(R_{xy}\) is to 0, the weaker the correlation between them. A strong correlation between two sensors means that they are interchangeable.
# Roman Urdu Sentiment Analysis Using Transfer Learning
## 2 Literature Review
### Attention-Based Network for Sentiment Analysis  
Despite considerable breakthroughs in sentiment analysis, deep neural networks' performance is inadequate [26; 42]. One of their typical problems is that they treat all words in phrases identically and cannot focus on critical sections [43]. The attention mechanism, with its proven ability to comprehend text effectively, has lately been utilized in a wide range of NLP applications, most notably sentiment analysis, to address this deficiency. Humans have a visual attention mechanism that helps them prioritize the information in a sentence that is most important to remember rather than encoding the entire thing. With text classification in mind, [PERSON] et al. [19] tweaked RNN by introducing a weight to fulfill the attention function. In addition, [PERSON] et al. [44] presented an attention-based LSTM Model that could concentrate on different sentence segments. [PERSON] et al. [45] suggested a domain attention model for multi-domain sentiment classification. To pick the best features relevant to each domain, their suggested model prioritized representations of those domains.  
A unique approach for sentiment analysis was developed by [PERSON] et al. [24], which uses eye-tracking data to learn about the viewer's mental state. To identify the positive and negative emotions in texts, [PERSON] et al. [46] used a sparse self-attention method to record the significance of each word. It was proposed by [PERSON] et al. [27] to use a sparse attention-based separable dilated convolutional neural network, wherein the properties of a particular target entity are used to identify sentiment-oriented components. An organizational framework for classifying emotions and extracting topics was proposed by [PERSON] et al. [6]. Without using aspect-level annotation, their model extracts aspect-sentiment clusters. The authors [PERSON] et al. [8] presented a sequence-to-sequence selective attention-based approach for the summarization of social text. They immediately optimized the ROUGE score using reinforcement learning and cross-entropy, and they employed a special gate to filter out the irrelevant data.  
Despite the apparent success of using an attention mechanism on DNN, relatively some studies have investigated its effects on CNN for use in sentiment analysis.
# Study on the Influence of Groundwater Variation on the Bearing Capacity of Sandy Shallow Foundation
## 1 Introduction
In this study, in view of the existing problems in the study of the influence of inundation on physical model tests, a bearing-capacity analysis was carried out. The sandy soil along the Yangtze River was used as the model foundation. The sand particles were fine, and the degree of saturation had an obvious influence on the engineering characteristics of the sand [22; 23]. A self-made water pump was used to control the water level of the model foundation, and the hydraulic servo system was used to load the model foundation, so as to obtain the bearing capacity of the model foundation under different water levels. The form and mechanism of the influence of groundwater-level variation on the foundation bearing capacity were examined. The influence of saturation on the strength and deformation index of sandy soil was analyzed. By carrying out the numerical simulation of foundation finite element at different water-table heights, the effect of the soil water content on the foundation bearing capacity was calculated, and the reliability of the model was verified. Finally, numerical simulation was used to predict the changes in the foundation bearing capacity under different water-level changes. Effective measures to prevent the decline of the foundation bearing capacity were put forward, combined with practical projects to give early warning of disasters caused by the decline of the foundation bearing capacity [24; 25; 26]. The specific research process is shown in Figure 1.
## 3 MRCIF Algorithm
### Physical Locator Sub-Algorithm  
The physical locator sub-algorithm is responsible for locating the load address of the code module in physical memory. DLL files are organized in the general format of PE files, whether normally loaded DLLs or covertly injected DLLs. These files need to be applied to memory pages in the virtual memory and loaded in blocks according to the PE file format. Meanwhile, these virtual memory pages must be mapped to physical memory before the DLL module can be executed.  
The DLL header code module is less than 4 KB and will be completely loaded at the beginning of a page in memory, and the characteristic string and relative offset remain unchanged. Thus, the PE header is also unchanged in the physical memory space (as shown in Figure 2).  
Therefore, a direct search for the PE header character can be performed in the physical memory \(P\) to locate the physical memory address where all DLLs are located. The process is shown in detail in Algorithm 1.  
```
Input: Physical memory page set \(P\) Output: DLL code module physical memory page set \(P_{e}\) Init: \(P_{e}\leftarrow\varnothing\) for each \(p\in P\) do: if is PEheader (\(p\)) = True then \(P_{e}\gets P_{e}\cup\{p\}\) end Function is PEheader (\(p\)): if \(p[0,1]\) = 4d5 ah then  e_flaew \(\leftarrow\)\(p[0\)x3c_...,0x3f] if \(p[\)e_flaew,..., e_flaew + 4\(]\) = 0x50450000 then  return True else return False
```  
**Algorithm 1**Code Module Physical Locator Algorithm
# WFO: Cloud-Edge Cooperative Data Offloading Strategy Akin to Water Flow
## 5 Experiment and Analysis
### The Performance Comparison of WFO, FQ, and FCFS
#### 5.3.3 WFO vs. FQ and FCFS: Impact of the Number of Nodes on Delay  
This section presents an experiment that examines the impact of the number of ECs on data delay. The experiment was conducted with CC bandwidths of 100 MB/s and 150 MB/s and a total offloaded data amount of 150 MB. The number of nodes ranged from 30 to 140, with a delay tolerance of 20 ms per node. Figure 9 displays the experimental result.  
As the number of nodes increased, both FQ and FCFS experienced significant increases in delays, while the delay of WFO remained stable between 60 s and 160 s. When the number of nodes was small, the delays of all three strategies were nearly equal because WFO could not benefit from serving a large number of users. However, it is essential to note that when there are many nodes, WFO can offload data for the same node through multiple nodes, resulting in a more negligible delay than the other two strategies. As the number of nodes increases, the delay of WFO decreases due to its ability to coordinate and significantly reduce the delay of subsequent nodes. Cooperative forwarding allows nodes to lower the entire time delay when calculating the average time delay. Additionally, WFO with HPC performs close to the optimal value, providing evidence of its effectiveness.