esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Comparison of the Proposed Approaches with the Literature
(2023) [14], that use the same dataset, it is seen that all other methods have higher classification accuracy except the AlexNet, Proposed MSU-CNN, and GoogLeNet models in this study when the dataset is divided into 80% training and 20% testing. In the study conducted by [PERSON] et al. (2022) [17], the same dataset as in this study was used, and it was split into 80% training and 20% testing. They reported that the success rates obtained from the AlexNet, VGG16, and VGG19 models were 94.42%, 98.84%, and 98.14%, respectively. They achieved the highest success rate with VGG16 at 98.84%. In this study, the VGG16, EfficientNet-B0, ResNet-18, Inception-V3, and ResNet-50 models achieved higher success rates than those reported in their study. In the study by [PERSON] and [PERSON] (2024) [16], which used a different dataset composed of Kirmizi and Siirt data types, a classification accuracy of 99.91% was achieved. They split the dataset into 80% training and 20% testing. Despite utilizing a different dataset, the proposed ResNet-50 model outperformed their model, achieving 100% accuracy, demonstrating its effectiveness. Additionally, the MSU-CNN model designed in this study outperforms [PERSON] et al. (2021) [21], [PERSON] (2023) [18], and [PERSON] et al. (2023) [14] using the same dataset in terms of classification accuracy. Moreover, the average classification success of the EfficientNet-b0, ResNet-18, Inception-v3, and ResNet-50 models with 5-fold cross-validation used in this study is higher than [PERSON] et al. (2021) [21], [PERSON] et al. (2022) [17], [PERSON] (2023) [18], and [PERSON] et al. (2023) [14] studies.
## 3 Proposed Scheme
### AMBTC Image Encryption  
In this section, we provide a detailed step-by-step explanation of the encryption process for the compressed image AMBTC. The method of compressing the original image with AMBTC is based on Section 2.1. It is assumed that the size of the image is \(N\times N\) and the size of one block is \(4\times 4\).  
**Input**: An AMBTC cover image \(I\) and key EK.  
**Output**: An encrypted cover image \(I^{\prime}\) with size \(N\times N\).
**Step 1:**: Read a \(trio(Q_{1},Q_{2},M)\) from the cover image \(I\), where \(Q_{1}\) and \(Q_{2}\) are the quantization levels and \(M\) is a bitmap \(M=(m_{1},\,m_{2},\cdots,\,m_{15},m_{16})\).  
**Step 2:**: The quantization levels are \(Q_{1}=(a_{8},a_{7}\ldots a_{1})\) and \(Q_{2}=(b_{8},b_{7}\ldots b_{1})\), where \(a_{8}\) is the Most Significant Bit (MSB) of \(Q_{1}\) and \(a_{1}\) is the LSB of \(Q_{1}\). Similarly, \(b_{8}\) is MSB of \(Q_{2}\) and \(b_{1}\) is the LSB of \(Q_{2}\). \(r=(r_{8},r_{7}\ldots r_{1})\) is pseudo-random binary generated by the encryption key EK. Encryption for two quantization levels \(Q_{1}\) and \(Q_{2}\) is obtained by using Equations (10) and (11).  
\[\begin{cases}\tilde{a}_{i,j}=a_{i,j}\oplus r_{i,j},\;\;j=0\ldots 7\\ \tilde{b}_{i,j}=b_{i,j}\oplus r_{i,j},\;\;j=0\ldots 7\end{cases} \tag{10}\]  
\[\begin{cases}Q_{1i}=\sum_{i=0}^{7}\tilde{a}_{i,j}\times 2^{j}\\ Q_{2i}=\sum_{j=0}^{7}\tilde{b}_{i,j}\times 2^{j}\end{cases} \tag{11}\]  
Here, \(i\) is the index of the block and \(j\) is the subscript for the two quantization levels \(Q_{1}\) and \(Q_{2}\) converted into binary numbers.
**Step 3:**: For a bitmap \(M=(m_{1},m_{2},\ldots,m_{16})\) of \(trio_{i}\), a pseudo-random binary number \(r\) is generated by the image encryption key EK. An encrypted bitmap block \(M^{\prime}\) is obtained by applying the XOR operation between the bitmap and the pseudo random bits \(r\) (Equation (12)).  
\[M^{\prime}=m_{j}\oplus r_{j},j=1\ldots 16 \tag{12}\]
**Step 4:**: If \(i\) is not the last block, it moves to _Step 1_ and repeats the given process. When the given procedure is completed, the encrypted AMTC is finally generated.  
When this process is completed, an encrypted cover image is finally created. Data hiding is then applied to the bitmap of each block of this encrypted cover image.
## 4 Numerical Results and Discussion
### Out-of-Plane Simulation
#### 4.1.3 Effect of Strain Rate and Size  
Figure 41 shows the stress-strain curves of the simulation under growing strain rates compared with the experimental result. The simulation maximum stress became higher as the strain rate became higher; this trend was the same as the experimental result. The specific simulation maximum stress values were similar to the experiment maximum stress, and the values are listed in Table 8. In addition, the simulation slopes of the non-linear and linear periods were similar to the experimental results. In the damaged situation shown in Figure 41a, the slope after failure was also similar to the experimental result. In the non-damaged situation shown in Figure 41a, the stress-strain curves returned after the maximum stress point along the loading path, unlike the experimental result. This was possibly because the FE model did not apply a cumulative failure model.  
Figure 41: The experimental and simulation results under different strain rates: (**a**) stress–strain curves; (**b**) first derivative of stress–strain curves.  
Figure 40: The contours of \(\Phi 9\times 4\) specimen at 125 \(\upmu\)s (unit: cm-\(\upmu\)s-g): (**a**) effective stress of sub-laminates; (**b**) effective strain of sub-laminates; (**c**) damage degree of cohesive elements.  
As the simulation results show in Figures 42a and 43a, under the same impact velocity, the thickness had little influence on the maximum stress and the slope of the stress-strain curve. However, the experimental results showed that, if the specimen was thicker, the slope of the non-linear period would be higher (see Figures 14a and 15a). According to Figure 35, if the specimen was more compact, the slope of the non-linear period would be higher. Therefore, this difference between the simulation and experimental results was possibly because the thicker specimen was more compact. Along the thickness of the composite plate, the middle part was more compact. The specimens could not be taken exactly from the middle part of the composite plate. As mentioned in Section 2.1, when making the specimens, one 5-mm specimen was taken from one small piece, whereas two 3-mm specimens two or 4-mm specimens were taken from one cut cube. Therefore, if the specimen was thicker, it was more likely to have a more compact part, while a thinner specimen was more likely to have a less compact part.  
As the simulation results show in Figures 42b and 43b, under the same impact velocity, the diameter had little influence on the slope of the stress-strain curve. If the diameter was smaller, the maximum stress would be higher. These two features were similar to the experimental result. As the simulation results show in Figures 42c and 43c, under the same impact velocity, the shape of the cross-section had little influence on the maximum stress and the slope of the stress-strain curve, which is similar to the experimental result. Table 8 shows
## 7 Decision Making
### Decision-Making Methods
#### 7.2.4 Fuzzy Logic Method  
Fuzzy set theory and fuzzy logic are very useful for solving decision-making problems with insufficient knowledge or imprecise data. Different from the probability theory, fuzzy logic theory expresses the uncertainty of truth in an explicit way. In the traditional sets an element is either included in the set or is not, while an element in a fuzzy set is included with a degree of truth normally ranging from 0 to 1 [239]. Meanwhile, fuzzy logic recognizes the lack of knowledge or absence of precise data, and describes most variables in linguistic terms, which make fuzzy logic models very intuitively similar to human reasoning. Ever since the first introduction to risk management [240], fuzzy logic has been widely applied in structure damage identification and decision making [102; 241; 242]. [PERSON] [243] developed a fuzzy logic system for health monitoring of a helicopter rotor blade, which is able to reduce the possibility of false alarms and help the maintenance engineer by roughly locating the damage area but accurate for further nondestructive inspections. [PERSON] et al. [244] presented a fuzzy based decision making methodology to handle the uncertainties and subjectivities so as to assess risks in complicated construction situations. Meanwhile, due to the feature in describing an event/result using natural language, the fuzzy logic approach would be particularly useful for remedying the uncertainties and imprecision in bridge inspectors' observations [245].
# A Systematic Literature Review of Autonomous and Connected Vehicles in Traffic Management
## 3 Research Analysis
### Traffic Management Methodologies Consisiting of Primary Goals
#### 3.2.9 Other: Data Sharing  
To reduce AIM's complexity and data exchange, an expanded version of AIM is described in [82]. The authors created the incremental data synchronization strategy known as ksync for driver agents to minimize data transfer and prevent redundancy from maximizing bandwidth utilization. According to experimental assessments, the average data compression rate can increase by more than 75%.
# An Adaptive Launch Control for Balloon-Borne UAVs with Large Wingspans
## 6 Evaluation
### Experimental Setup  
Experiment SettingsWe first provide the experiment's settings. For the simulation test, the simulation conditions are as follows: the height is 20,000 m and velocity is \([3,2,3]^{T}\) m/s. Considering the acceleration of the aircraft and the safety of the process, the initial pitch angle is \(-76\) deg, and the rotation angle's rate is \(3\) deg/s.  
Apart from the experiment's settings, the rotation angle rate of the UAV tied with the balloon affects the roll rate and yaw rate under the body's coordinate frame. In addition, the increase in speed under the initial coordinate will further affect the speed components of the x-axis, y-axis, and z-axis under the body's coordinate frame. Thus, we provide two coordinate transformations.  
Rotation speed of the platformConsidering the influence of the rotation of the balloon's platform at the initial time, the following conversion relationship can be obtained by transforming the rotation angle's rate under the inertial system into the body's axis system.  
\[\left[\begin{array}{c}p\\ q\\ r\end{array}\right]=\left[\begin{array}{ccc}1&0&-sin\theta\\ 0&cos\phi&cos\theta sin\phi\\ 0&-sin\theta&cos\phi cos\theta\end{array}\right]\left[\begin{array}{c}\dot{ \phi}\\ \dot{\theta}\\ \dot{\psi}\end{array}\right] \tag{8}\]  
where \([p\;q\;r]^{T}\) is the roll rate, pitch rate, and yaw rate in the body coordinate, \([\dot{\phi}\;\dot{\theta}\;\dot{\psi}]^{T}\) is the pitch angle, roll angle, and yaw angle.  
Platform rising speedThe coordinate conversion matrix from the initial coordinate to the body's coordinate is described as follows:  
\[\left[\begin{array}{c}u\\ v\\ w\end{array}\right]=\left[\begin{array}{ccc}cos\theta cos\psi&cos\theta sin \psi&-sin\theta\\ sin\theta sin\phi cos\psi-cos\phi sin\psi&sin\theta sin\phi sin\phi sin\phi cos \phi\\ sin\theta cos\psi cos\psi-sin\theta cos\psi&-sin\theta cos\psi+sin\theta cos\phi sin \psi&cos\theta cos\theta\end{array}\right]\left[\begin{array}{c}V_{N}\\ V_{E}\\ V_{U}\end{array}\right] \tag{9}\]  
where \([u\;v\;w]^{T}\) is the speed with respect to the body's coordinate, and \([V_{N}\;V_{E}\;V_{D}]^{T}\) is the speed with respect to the initial coordinate.  
BaselinesIn order to evaluate the effectiveness of our proposed strategy, we compare the simulation experiment to the control algorithm utilized in the latest physical experiment [22]. The settings of physical experiment are as follows: The aim height is 2000 m, and the pull-up control algorithm utilizes the exponential pitch-angle-based model.  
MetricsWe utilize the maximum deviation as the evaluation basis, which is \(\delta=|y_{max}-y|\). We do not use the steady-state's time that the control system spend becoming steady mainly because the aircraft has flexible structures. When stability is attained quickly, the aircraft may fracture more easily.
###### Abstract  
In this paper, a new dedicated multi-channel ionospheric oblique backscatter sounder is described. A miniaturized L-shaped antenna array was employed for the receiving of the oblique backscatter echoes in the present system. Firstly, two typical adaptive beamforming algorithms were introduced to improve the anti-jamming ability. Then, simulations were carried out to verify the beamforming performance in azimuth and elevation simultaneously. Furthermore, the experimental results by the present sounding system were used to test the performance of the adaptive beamformers. Results show that the radio frequency interference and the interference of the vertical echoes can be effectively suppressed by the adaptive beamformers. In particular, the use value of the beamforming in the receiving of the ionospheric oblique backscatter sounding is described in detail through the analysis of the signal sequences in several typical frequencies. And after the constant false-alarm rate processing, the oblique backscatter ionograms processed by the adaptive beamforers have clearer and continuous leading edge compared with the original ionograms. As a result, the adaptive beamformers of great significance to improve the detection ability of the ionospheric oblique backscatter sounding.  
Keywords:ionospheric oblique backscatter sounding; miniaturized L-shaped antenna array; adaptive beamforming +
Footnote †: journal: Remote Sensing
# Federated Learning-Based Service Caching in Multi-Access Edge Computing System
## 5 Experiment and Evaluation
### Metric  
Extensive experiments are conducted to evaluate the performance of the proposed model under multiple scenarios. Two metrics are used to comprehensively describe the performance comparison results: the service hit ratio and service delay.  
The service hit ratio indicates the capacity to choose a service that aligns with user demands. A service hit is captured when the requested service is installed properly on the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of MEC servers & 64 \\ Number of users & 300 \\ Number of all services & 500 \\ Computational capacity of MEC server & 36 GHz \\ User time slot & 2 min \\ Operation time slot & 60 min \\ Backhaul network bandwidth & 150 Mbps \\ Request data size & \([0.05,5]\) Mb \\ Required computational resource of service & \([0.1,1]\) GHz \\ Skewness parameter & 0.8 \\ Input layer of AE & 500 \\ Latent space of AE & 23 \\ Learning rate & \(10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
MEC server. Conversely, if a service is not deployed on an MEC server, it is classified as a service miss. \(SH\) and \(SM\) denote the total counts of service hits and misses, respectively, in the system. Hence, the service hit ratio \(SHR\) is determined as follows:  
\[SHR=\frac{SH}{SH+SM}. \tag{15}\]  
The primary objective of this study is to determine service location decisions using historical user request data. Hence, the delay serves as a measure to evaluate the performance of the proposed method rather than being utilized to formulate an optimization problem. In this study, we investigate delays resulting from the unavailability of requested services on a local MEC server. When the computation is offloaded to the cloud server, there is an increase in the duration required to transmit the request information as opposed to processing on the local MEC server.  
The data transfer rate in a wired network between MEC and the cloud server corresponds to the link bandwidth. The uplinks from MEC to the cloud server are used to transmit the task data. \(L\) denotes the wired uplink transmission latency from MEC to the cloud server, calculated as follows:  
\[L=\frac{d_{u}(t)}{W_{u}}, \tag{16}\]  
where \(d_{u}(t)\) and \(W_{u}\) denote the task data size and uplink channel bandwidths allocated to the wired link between MEC and the cloud server, respectively.  
It is noteworthy that the testing phase is conducted using a certain number of operation time slots. Consequently, determining the service delay and service hit ratio involves computing the average value experienced by the entire system during each operation time slot.
# Nucleotides as Bacterial Second Messengers
## 5 The c-di-AMP Nucleotide
### c-di-AMP Receptors  
The first c-di-AMP receptor was the Ms5346 protein from _Mycobacterium smegmatis_ and corresponds to a regulator of the tetracycline resistance family (TetR) that was later renamed the c-di-AMP receptor regulator (DarR). In _M. smegmatis_, DarR binds to its own promoter and thus represses its transcription. DarR binds to the promoters of the medium-chain acyl-CoA ligase transcription operon and the family of prominent transporter facilitators. The c-di-AMP-DarR complex causes increased interactions with DNA [79], so changes in the level of c-di-AMP affects fatty acid synthesis in _M. smegmatis_.  
In _S. aureus_, KtrA, KdpD, PstA, CpaA, and OpuCA are c-di-AMP target proteins [82]. CpaA is a predicted cation/proton antiporter protein, and OpuCA is the ATPase component of the ATP-to-carnitine binding cassette transporter OpuC [83].  
The KtrA protein is a member of the Ktr-type potassium transport system [1]. KtraA binds with KtrB-type membrane components to form a potassium transport system, which has been detected in various bacteria [84]. The c-di-AMP binds to the KtrA protein, causing a conformational change in the transporter components that ultimately make the KtrB protein able to open or close the potassium transporter channel. In addition, bacteria mutants in \(gdpP\) gene, which produce high levels of c-di-AMP, are sensitive to salt stress and, similar to ktrA mutant strains, require high potassium levels to grow under osmotic stress conditions [78].  
The c-di-AMP binds to the histidine kinase protein KdpD to control its function [85]; the KdpD protein is involved in potassium homeostasis [86]. KdpD protein and KdpE induce the expression of a second type of potassium uptake system [87].  
Another c-di-AMP receptor protein is PstA, which responds to cells' nitrogen and carbon status by sensing glutamine and 2-ketoglutate levels [73; 88].
## 3 Management of Electrical Grids by Means of WSANs
### Application of WSANs to Manage the Whole Electrical Grid
#### 3.2.1 WSANs for Power Generation Management  
In the conventional electrical grid, wired sensor technologies are largely used in power plants to monitor critical parameters of power generators, such as current, voltage or temperature. Because of their onerous installation and maintenance costs, these sensors are not suitable to supervise the increasing number of renewable energy generation installations, such as solar or wind farms. Nevertheless, the penetration of renewable energies in the future electrical grid and, more particularly in MGs, mainly depends on the capacity of the grid to monitor this number of generation facilities that often operate in remote areas and in harsh environments. Therefore, due to the variability of renewable energy production, it is necessary to implement real time data collection platforms in order to predict, as far as possible, the potential generation level. The collected data can be thus employed not only to minimize the impact caused by the intermittent generation, but also to optimize the energy storage when the generation is higher than the energy consumption.  
In this context, WSANs play a key role in order to remotely monitor, via wireless communication networks, renewable power generation systems at low cost and offering a flexible and simple deployment [30]. Hence, WSANs could be used to collect ambient data such as wind speed and solar irradiance in order to optimize wind or photovoltaic generation applying different predictive algorithms [31; 32]. Another interesting application of WSANs is the early detection and correction of eventual failures of the generation units. As addressed in [33], wireless accelerometers are used to measure the vibrations of wind turbine blades with the goal to anticipate the maintenance tasks. If the mechanical stress suffered by the blades is excessive, the actuators can modify their orientation or, in the worst case, stop the wind turbine. The data collected by the sensors can also be utilized to detect the disconnection of a MG from the main grid, and to ensure the balance between produced and consumed energy, in grid connected mode or in islanding [34]. Finally, some research works, as in [35], have discussed the possibility of using WSANs instead of conventional wired sensor solutions in industrial power plants to predict and evaluate the energy production.
# Real-Time Interactive Parallel Visualization of Large-Scale Flow-Field Data
###### Abstract  
With the increasing demand for high precision in numerical simulations using computational fluid dynamics (CFD), the use of large-scale grids for discretized solutions has become a trend, resulting in an explosive growth of flow-field data size. To address the challenges posed by large-scale flow-field data for real-time interactive visualization, this paper proposes novel strategies for data partitioning and communication management. Firstly, we propose a data-partitioning strategy based on grid segmentation. This approach constructs metadata to create file viewpoints for each process and performs grid partitioning. Subsequently, it reconstructs sub-grids within each process and utilizes a coordinate-mapping algorithm to map global coordinates to local process coordinates, facilitating access to attribute variables through a lookup table. Secondly, we introduce a real-time interactive method for large-scale flow fields. This method leverages the system architecture of high-speed interconnection among compute nodes in a cluster environment and low-speed interconnection between service nodes and rendering nodes. It enables coordinated management of parallel rendering and synchronized rendering methods. The experimental results on typical flow-field data demonstrate that the proposed data-partitioning strategy improves the loading speed of millions of grid-level data by a factor of 7, surpassing ParaView's performance by 1.5 times. Furthermore, it achieves system load balancing. Real-time interaction experiments with datasets containing 500 million and 800 million grid cells exhibit millisecond-level latencies, demonstrating the effectiveness of the proposed communication management method in meeting the real-time interactive visualization demands of large-scale flow-field data.  
data partitioning; scientific visualization; large-scale flow-field data; EnSight Gold data format; real-time interaction; parallel visualization +
Footnote †: journal:  
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