esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Timing Error System Model  
Each transmitted symbol in a binary phase shift keying (BPSK) system is composed of \(N\) point impulse responses \(g(mT)\) from a square root raised cosine (SRRC) pulse shaping filter with \(L\) times sampling rate, where \(T\) is the symbol interval. The signals are shaped as sequences (\(b_{i}\)) in the above filter, where \(a_{i}\in\{\pm 1\}\) is the \(i\)-th modulated symbol and \(b_{i}\) is the sample symbol interpolated with (\(L-1\)) zeros between every two adjacent \(a_{i}\) to fit the sampling rate. \(\tau_{m}\) is the timing offset to influence the \(m\)-th sample. Thus, the signal \(s(mT)\) for transmission is given by  
\[s(mT)=\sum_{i=0}^{N-1}b_{i}\cdot g(mT+\tau_{m}-iT/L). \tag{1}\]  
A received sequence with \(L\)-times up-sampling rate without timing offsets is expressed as  
\[r(kT/L)=s(kT/L)+n(kT/L), \tag{2}\]  
where \(r(kT/L)\) is the received signal, \(n(kT/L)\) is the additional white Gaussian noise (AWGN) from the channel, and \(T/L\) is the sampling interval. The assumed time reference for the \(m\)-th sample at the receiver when timing errors occur differs from the corresponding sample at the transmitter with timing offset \(\tau_{m}\). The \(m\)-th sampled signal after \(L\) times down-sampling is presented as  
\[r(mT)=s(mT+\tau_{m})+n(mT). \tag{3}\]  
Finally, \(r(mT)\) is obtained by replacing \(s(\cdot)\) with the above expression. The resulting equation is presented as follows.  
\[r(mT)=\sum_{i=0}^{N-1}b_{i}\cdot g(mT+\tau_{m}-iT/L)+n(mT). \tag{4}\]  
Constant timing frequency offset and random walk [4] can be combined in our model, because proper timing error models exist as constant timing phase offsets. The combined equation is presented as  
\[\tau_{m}=\tau_{m-1}+T\cdot N(0,\sigma_{\tau}^{2})+T\cdot\upsilon/10^{6},(\tau_ {0}=D), \tag{5}\]  
where the initial time instance is offset by constant timing phase offset \(D\), the timing error is disturbed by a zero mean Gaussian random variable (random walk) with variance \(\sigma_{\tau}^{2}\) denoted by \(N(0,\sigma_{\tau}^{2})\), and the constant timing frequency offset \(\upsilon\) is measured in parts per million (\(ppm\)). They should be estimated and compensated precisely for high transmission effect.
# Decision-Making Framework for Aviation Safety in Predictive Maintenance Strategies
## 4 Discussion
### Challenges and Limitations of the Study  
The implementation of the PM framework in the aviation industry offers significant potential benefits, but it also comes with several challenges and limitations that must be addressed to ensure its success. These challenges arise from technical, operational, organizational, and regulatory factors, requiring careful consideration and mitigation strategies.  
A primary challenge is the availability and quality of data. The framework relies on accurate and comprehensive historical maintenance records, operational performance metrics, and degradation patterns. However, many components lack sufficient data due to limited sensor integration or inconsistent data collection practices. Additionally, historical data may be incomplete, outdated, or biased, and standardization across datasets from multiple fleets, manufacturers, or MRO providers can be difficult.  
Organizational resistance and the need for effective change management also pose challenges. Transitioning to a PM framework requires significant cultural and procedural shifts, which may face resistance from maintenance teams accustomed to traditional reactive or preventive approaches. Stakeholders may question the reliability of predictive models and worry about potential disruptions to established workflows, emphasizing the importance of building confidence and acceptance through strategic change management.  
Regulatory and compliance constraints further complicate the implementation of PM. The aviation industry operates under strict regulatory oversight, and PM methodologies must align with mandated inspection intervals and maintenance practices. Securing regulatory approval for PM strategies can be resource-intensive, requiring thorough validation and documentation to demonstrate compliance.  
Expert judgments can be subjective, leading to variability in results. Small changes in weights or scores during the sensitivity analysis may affect component rankings, raising concerns about the stability of outcomes. Ensuring consistency and validation across different scenarios is essential for maintaining the framework's credibility.  
While these challenges may limit immediate adoption, they also present opportunities for innovation and collaboration. By recognizing and mitigating these limitations, the framework can contribute to a more resilient, efficient, and sustainable aviation maintenance ecosystem, ultimately optimizing strategies to enhance safety, reliability, and cost-effectiveness.
## 2 Materials and Methods
### HPSO Algorithm with Penalty Function
#### 2.2.1 Individual Coding  
The algorithm adopts a single chromosome encoding method. Each particle includes a coding scheme. First, \(Z\) denotes a set of segmentation numbers, \(\mid Z\mid=l-1\), where \(\mid Z\mid\) represents the number of elements in set \(Z\). A chromosome is denoted as \(S=<\)x\({}_{1}\), \(\cdots\), \(x_{i}\), \(\cdots\), \(y_{1}\), \(\cdots\), \(y_{i}\), \(\cdots\), \(z_{1}\), \(\cdots\), \(z_{o}\)\(>\), where \(x_{1}\)-\(x_{i}\in X\) are the indexes of unloading task points, \(y_{1}\)-\(y_{j}\in Y\) are the indexes of loading task points, and \(z_{1}\)-\(z_{o}\in Z\) are the segmentation numbers. The segmentation numbers are used to segment the chromosome to obtain scheduling sequences of AGVs.  
For example, we assume that an unloading task point set is {\(x_{1}\), \(x_{2}\), \(x_{3}\), \(x_{4}\), \(x_{5}\)}, a loading task points set is {\(y_{1}\), \(y_{2}\), \(y_{3}\), \(y_{4}\), \(y_{5}\)}, and a segmentation number set is {\(z_{1}\), \(z_{2}\), \(z_{3}\)}. The chromosome is \(S=<\)x\({}_{1}\), \(x_{2}\), \(x_{3}\), \(x_{4}\), \(x_{5}\), \(y_{1}\), \(y_{2}\), \(y_{3}\), \(y_{4}\), \(y_{5}\), \(z_{1}\), \(z_{2}\), \(z_{3}\)\(>\). The three segmentation numbers divide the chromosome into four parts, as shown in Figure 2. Each part represents a scheduling sequence of an AGV, denoted by \(S_{1}\)-\(S_{4}\), respectively. \(S_{1}\)'s scheduling sequence is \(<\)x\({}_{4}\), \(y_{2}\)-, \(S_{2}\)'s scheduling sequence is \(<\)x\({}_{5}\), \(y_{4}\)-, \(S_{3}\)'s scheduling sequence is \(<\)x\({}_{1}\), \(y_{1}\), \(x_{3}\), \(y_{3}\)\(>\), and \(S_{4}\)'s scheduling sequence is \(<\)x\({}_{2}\), \(y_{5}\)-?. The AGVs' scheduling sequence is shown in Figure 3.  
Figure 3: AGVs’ scheduling sequence.  
Figure 2: Individual coding mode.
# Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis
## 2 Analysis of the Influence of the Dynamics of the PTS on the Operation of the VTS
### Scheme of Operation and Calibration of the SMTF
When new data from the PTS are received (either from the video stream analysis system or from the connected vehicle's sensors), they are stored in the global database and then distributed to local databases based on the location of the vehicle when the data were collected. The accumulation of these new data from the PTS happens over a period of time denoted as \(\tau_{S}\). The process of calibrating the SMTF was started when we received a new dataset that included a time series of vehicle characteristics. Each sample in the dataset contained information about the time and geographic coordinates of its recording. It allowed us to distribute the data across local areas within the modeled region, providing insight into the overall trend across the modeling area. Once the new data are collected, extracting key characteristics begins by calculating the trend of the new data \(T_{n}{}^{\prime}(t)\) and relevant test statistics \(T_{n}(t)\). This involves determining the maximum absolute difference between two trends for comparison, denoted as \(\max(S_{n}(t))\)  
Figure 4: The process of training local models when calibrating the global SMTF model. The decision to initiate global or local calibration is based on specific conditions:  
\[\sup_{t}\left|T_{n}^{\prime}(t)-T_{n}(t)\right|>S_{n}(t) \tag{2}\]  
If these conditions are met, additional training is conducted on the existing global ET model. The updated model is then incorporated into each sector by additional training on a local dataset, similar to the third stage of constructing the original SMTF. This additional model training occurs in parallel with the operation of the replaced models without causing any interruption. Additional training is only performed on a specific local model if a shift in local data is detected. Calibrating the SMTF model involves training multiple copies of the global ET model on local data for each cluster. It happens when the acceptable deviation threshold of the statistical criterion for the distribution of the vehicle characteristics being considered is exceeded. Evaluating the simulation's adherence to the established and calibrated SMTF involves a comprehensive process. It involves a statistically comparing the distributions of generated quantities with those obtained from the PTS data. Additionally, the performance of the ML algorithm, which has been extensively trained and integrated into the VTS, is assessed using a test dataset from the PTS. The results of these evaluations are presented in Section 4.
## 3 Results
### Secondary Evaluation and Testing at Large-Scale  
Based on the results obtained from the primary evaluation and testing, fraction (a) (namely, whole fermentation broth) was evaluated in a large-scale trial. This fraction is now termed prototype 1. Dry density versus moisture content graph is shown (Figure 5). As in Table 4 the preliminary assessment on one soil type in this screen, shows resistance to abrasion (3.37 \(\pm\) 0.03%) (\(p\) value \(\leq\) 0.0001) and resistance to erosion (33.20 \(\pm\) 0.15%) (\(p\) value \(\leq\) 0.001) using 18 mL dosage of the bio-stabiliser at large-scale (Figures S3 and S4 (Supplementary Material)). From the statistical comparison, the results show the large-scale test results, as well as the mean values obtained for the positive and negative controls. The tests utilise loss of material as a primary measure in both methods. At 14 mL dosage the resistance to abrasion and erosion declines. Therefore, by using an increased concentration, the stabiliser effect improves (see Figure 6).  
Figure 5: Dry density versus moisture content graph (**a**) Permazyme product, (14 mL), (**b**) Permazyme product (18 mL), (**c**) Prototype, (14 mL), (**d**) Prototype, (18 mL).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**1.4\% Concentration**} & \multicolumn{4}{c}{**1.8\% Concentration**} \\ \hline
**Strength Test** & **Mean (\% Loss)** & **CV \%** & \(p\) **Value** & **Mean (\% Loss)** & **CV \%** & \(p\) **Value** \\ \hline  & \multicolumn{5}{c}{Abrasion test} \\ \hline Neg C—after & 4.90 & 1.25 & \textless{}0.0001 & 4.90 & 1.25 & \textless{}0.0001 \\
250 revs & 11.20 & 0.09 & \textless{}0.0001 & 11.20 & 0.09 & \textless{}0.0001 \\ Pos C—after & 4.467 & 3.42 & 0.0004 & 4.00 & 0.00 & \textless{}0.0001 \\
250 revs & 8.700 & 1.15 & \textless{}0.0001 & 7.800 & 1.25 & \textless{}0.0001 \\ Prototype & 1—after 250 & 4.333 & 10.66 & 0.004 & 3.367 & 1.71 & \textless{}0.0001 \\ \multicolumn{2}{c}{revs} & & & & & \\ Prototype & 1—after 500 & 10.07 & 0.57 & \textless{}0.0001 & 9.067 & 0.064 & \textless{}0.0001 \\ revs & \multicolumn{5}{c}{Erosion test} \\ \hline Neg C & 47.00 & 0.12 & \textless{}0.0001 & 46.90 & 0.02 & \textless{}0.0001 \\ Pos C & 25.50 & 0.02 & \textless{}0.0001 & 22.60 & 2.34 & 0.0002 \\ Prototype 1 & 36.20 & 0.48 & \textless{}0.0001 & 33.20 & 0.80 & \textless{}0.0001 \\ \hline \hline \end{tabular}  
* Key: Neg C—negative control; Pos C—positive control; revs—revolutions.  
\end{table}
Table 4: Large-scale industry testing showing 1.4% and 1.8% concentration of stabiliser (i.e., Prototype 1: whole fermentation broth from batch one) tested on soil type ii.  
Figure 6: Large-scale testing of the best prototype showing (**a**) abrasion and; (**b**) erosion test results. Results expressed as mean \(\pm\) SD; N = 3; \(p\) value (two-tailed), \(p\) < 0.05 vs. control.
## 5 Performance Evaluation
### Experiment 3  
For a more thorough understanding of the performance of BLJCAR in multiple rate case, the rate of one of both flows is fixed. In this experiment, the rate of flow 1 is fixed to 300 kbps and the rate of flow 2 varies from 300 kbps to 600 kbps with step of 20 kbps. Figure 9a-d shows the jitter performance of flow 1, network throughput, average end-to-end delay and packet loss ratio, respectively.  
Figure 8: **(a)** Average packet jitter _versus_ offered load; **(b)** Network throughput _versus_ offered load; **(c)** Average end-to-end delay _versus_ offered load; **(d)** Packet loss ratio _versus_ offered load.  
Figure 9: _Cont._From Figure 9, it is seen that BLJCAR can improve the jitter, network throughput, average end-to-end delay and packet loss ratio as compared with the ONC policy.
## 5 Conclusions  
A hybrid NFIHC combination of both the properties of FL and ANN controllers was proposed for STF-based SH-WPBS. STF is used to produce SYP for SH-WPBS avoiding conventional PLL in addition to the splitting of FC and HC of currents. The STF with transformations was also given in addition to the design of NFIHC. The proposed method was able to lower THDs within acceptable values, boost PF to almost unity, and maintain stable DC-Link voltage after studying five test scenarios with diverse load combinations, and balanced and unbalanced source voltages. Moreover, these performances were much better than those of the existing controllers of PI, FL, and ANN. The NFIHC also maintains a constant DC-link voltage for all variations in wind velocity. To validate the proposed method, the obtained %THD was compared with those of the latest methods that are available in the literature. As a part of future research, the proposed model might be tested on an island condition. In addition, the hybrid controller technique can also be implemented in the series active power filter.  
Conceptualization, S.K. and K.J.K.; methodology, S.R.S.; software, S.K.; validation, S.K., K.J.K., and S.R.S.; formal analysis, S.K.; investigation, K.J.K.; resources, S.R.S.; data curation, S.K.; writing--original draft preparation, K.J.K.; writing--review and editing, S.K., K.J.K., and S.R.S.; visualization, S.K.; supervision, K.J.K. and S.R.S.; project administration, S.K.; funding acquisition, S.K., K.J.K., and S.R.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"Woosong University's Academic Research Funding--2022\".  
Not Applicable.  
The authors declare no conflict of interest.  
Figure 21: THD spectrum.
# Wideband Circularly Polarized Antenna Based on a Non-Uniform Metasurface
###### Abstract  
This paper presents a non-uniform metasurface (MS)-based circularly polarized (CP) antenna that is able to perform with a wideband operation characteristic. A squared patch with truncated corners was chosen as a radiating CP source. Then, unlike the conventional CP MS antennas with a uniform MS, the proposed design employed a non-uniform MS placed above the driven patch. Apart from increasing the impedance bandwidth, the non-uniform MS was also capable of generating two additional CP bands in the high-frequency region, which contributed to significantly increasing the antenna's overall performance. For demonstration, an antenna prototype was fabricated and experimentally tested. The measured operating bandwidth of the fabricated antenna was about 30% and the broadside gain within this band was around 6.6 dBic. Compared to the other reported CP MS antennas in the open literature, the proposed design has the advantages of very wideband operation with a comparable size and gain.  
circular polarization; non-uniform metasurface (MS); patch antenna; wideband operation +
Footnote †: journal: Applied Physics Letters
# An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions
## 2 Literature Review  
Cloud computing has its roots in several fundamental concepts and technologies. In the 1960s, [PERSON], a prominent computer scientist, envisioned an \"Intergalactic Computer Network\" that allowed remote access to programs and data, laying the groundwork for distributed computing models. In the 1990s, grid computing emerged as a way to harness geographically dispersed resources for computationally intensive tasks. The Globus Toolkit, developed by [PERSON] and [PERSON], provided a software infrastructure for managing computational grids. Utility computing also emerged during this time, offering computing resources as a pay-per-use utility. Sun Microsystems and Amazon introduced utility computing models, demonstrating the benefits of on-demand resource rental.  
The phrase \"cloud computing\" first appeared in the mid-2000s, revolutionizing how computer resources are distributed and utilized. Amazon Web Services (AWS) established Infrastructure as a Service (IaaS) in 2006, providing virtualized servers, storage, and networking capabilities via the Internet. This revolutionized traditional hosting paradigms by introducing scalable and adaptable cloud-based infrastructure. The National Institute of Standards and Technology (NIST) was instrumental in standardizing cloud computing, describing it in 2011 as a concept that allows on-demand network access to a shared pool of programmable computing resources. NIST emphasized the model's ease of use and universality. Extensive study has been conducted to examine many views on cloud computing, offering insight into its consequences and potential.
## 3 Case Study
### Modelling and Parameter Identification
In order to verify the correctness of the method, we selected five points evenly in the interval of the fitting curve, obtained \(E_{1},G_{1},E_{2}\) and \(G_{2}\) through the parameter identification method, and put these values into the curve for verification. In Figure 16, the red dots are the verification points. It can be seen that the error between the red dots and the fitting curve is small, which proves the effectiveness of the method. The curve obtained by this method is of high precision when a certain number of points and intervals are reasonably selected for fitting. The more points used, the more accurate the curve. Through curve fitting, the relationship between the elastic modulus \(E_{1}\) and \(E_{2}\) and the shear modulus \(G_{1}\) and \(G_{2}\) under different pre-tightening torque was obtained. The resulting curves can guide the modeling of the same type of bolted joints. The specific operation is as follows: (_i_) The torque wrench can be used to determine the pre-tightening torque of the bolt. (_ii_) The parameter values of the thin layer element material \(E_{1}\), \(G_{1}\), \(E_{2}\) and \(G_{2}\) are drawn from the relation curves. Thus, an accurate dynamic model of the bolted joint structure could be established.
# Combined Self-Attention Mechanism for Chinese Named Entity Recognition in Military
## 3 BiLSTM-Self-Attention-CRF Model
### Bi-LSTM Encoding Layer  
#### 3.2.1 Lstm  
Long short-term memory (LSTM) is a special class of recurrent neural network (RNN). It is designed to solve the problem of gradient disappearance or gradient explosion when processing long sequence data and better model the long-distance dependency relationship than general RNN ([PERSON] et al., 2014). The hidden layer of LSTM has three special gating mechanisms, including the forget gate, input gate, and output gate. Three gates can be used to control the update, attenuation, input, and output of information in memory cells. In short, the main idea of LSTM is to manage the information in the memory unit by learning the parameters of the three gates, so that useful information can be stored in the memory unit after a long sequence.  
At a certain time \(t\), the forget gate determines the amount of information discarded by the cell, which is given by:  
\[f_{t}=\sigma(W_{f}\cdot h_{t-1}+U_{f}\cdot x_{t}+b_{f}) \tag{1}\]  
where \(x_{t}\) is the input of the current moment, \(h_{t-1}\) is the output of the hidden layer at the last moment, \(U_{f}\) represents the weight of the input information, \(w_{f}\) represents the weight of the forget gate, and \(b_{f}\) is the bias term. The input gate is to determine how much information the LSTM will save when the cell is updated, which is given by:  
\[i_{t}=\sigma(W_{i}\cdot h_{t-1}+U_{i}\cdot x_{t}+b_{i}) \tag{2}\]  
\[\widetilde{C}_{t}=\tan h(w_{c}\cdot h_{t-1}+U_{c}\cdot x_{t}+b_{c}) \tag{3}\]  
\[c_{t}=f_{t}c_{t-1}+i_{t}\widetilde{C}_{t} \tag{4}\]  
where \(U_{i}\), \(W_{i}\), \(U_{c}\), and \(W_{c}\) are the parameter matrices, \(b_{i}\) and \(b_{c}\) represent the bias parameters, \(C_{t}\) is the current cell state, and \(C_{t-1}\) is the state of the cell at the previous moment. The output gate ultimately determines the output vector of the LSTM model, which is given by:  
\[o_{t}=\sigma(W_{o}\cdot h_{t-1}+U_{o}\cdot x_{t}+b_{o}) \tag{5}\]  
\[h_{t}=o_{t}\star\tanh(c_{t}) \tag{6}\]  
where \(W_{o}\) and \(U_{o}\) are the parameter matrices, \(b_{o}\) is the bias parameters, and \(h_{t}\) is the final output of the current LSTM.
###### Abstract  
Visual navigation is an important guidance method for industrial automated guided vehicles (AGVs). In the actual guidance, the overexposure environment may be encountered by the AGV lane image, which seriously reduces the accuracy of lane detection. Although the image segmentation method based on deep learning is widely used in lane detection, it cannot solve the problem of overexposure of lane images. At the same time, the requirements of segmentation accuracy and inference speed cannot be met simultaneously by existing segmentation networks. Aiming at the problem of incomplete lane segmentation in an overexposure environment, a lane detection method combining image inpainting and image segmentation is proposed. In this method, the overexposed lane image is repaired and reconstructed by the MAE network, and then the image is input into the image segmentation network for lane segmentation. In addition, a convolutional skip triple attention (CSTA) image segmentation network is proposed. CSTA improves the inference speed of the model under the premise of ensuring high segmentation accuracy. Finally, the lane segmentation performance of the proposed method is evaluated in three image segmentation evaluation metrics (IoU, F1-score, and PA) and inference time. Experimental results show that the proposed CSTA network has higher segmentation accuracy and faster inference speed.  
lane detection; image segmentation; image inpainting; AGV