esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 An Adaptive Attitude Estimation Method Based on Multi-Sensor Fusion
### Phase-Lag-Free Low Pass Filter  
Phase lag error will be introduced in the above attitude estimation process based on Kalman filter. An additional high-order signal with phase advanced information is introduced to filter the original signal. The phase lag can be compensated for by phase-lag-free LPF.  
Define  
\[\alpha_{k}=\left[\begin{array}{cccc}\gamma^{\prime}_{k}&\theta^{\prime}_{k}& \psi^{\prime}_{k}\end{array}\right]^{T}, \tag{23}\]  
\[\beta_{k}=\left[\begin{array}{cccc}\gamma_{k}&\dot{\theta}_{k}&\dot{\psi}_{k }\end{array}\right]^{T}, \tag{24}\]  
where \(\gamma^{\prime}_{k},\theta^{\prime}_{k},\psi^{\prime}_{k}\) are the 3-axis angular attitude calculated by Kalman filter in Section 3.3 and \(\dot{\gamma}_{k},\dot{\theta}_{k},\dot{\psi}_{k}\) are the 3-axis angular velocity calculated from gyro in Section 3.1. These two inputs are original signals with noise.  
Figure 4 shows the design of this filter. The filter can be described as two sensors which could measure the nearby order information. During data process, \(\alpha_{k}\) should be the position signal when \(\beta_{k}\) is the velocity signal. In addition, if \(\alpha_{k}\) is the velocity signal, \(\beta_{k}\) should be the acceleration signal.  
In this research, the signal \(\alpha_{k}\) is low-pass filtered and the phase lag is corrected by \(\beta_{k}\). The parameter \(g\) is cut off frequency of LPF. The tracking characteristic of output signal \(\varphi_{k}\) is strengthened with the increase in \(g\). If \(\beta_{k}=\frac{d}{dt}\alpha_{k}\), the relationship \(\varphi_{k}=\alpha_{k}\) is met.  
Define  
\[\left\{\begin{array}{l}\dot{\varphi}_{k1}=g(\alpha_{k1}-\varphi_{k1})\\ \dot{\varphi}_{k2}=\beta_{k1}-g\varphi_{k2}\end{array}\right.. \tag{25}\]  
The output of system in Figure 4 can be expressed as  
\[\varphi_{k}=\left[\begin{array}{cccc}\gamma^{\prime\prime}_{k}&\theta^{ \prime\prime}_{k}&\psi^{\prime\prime}_{k}\end{array}\right]^{T}=\varphi_{k1}+ \varphi_{k2}, \tag{26}\]  
where \(\gamma^{\prime\prime}_{k},\theta^{\prime\prime}_{k},\psi^{\prime\prime}_{k}\) are the 3-axis angular attitude processed by phase-lag-free LPF. It can be seen that the design is composed of LPF and can greatly suppress the input noise from Equation (25).
## 1 Introduction
Based on the background of the integrated service requirements for ICNT in the NS, and taking into account the particularity of the NS atmospheric environment, it is necessary to analyze the physical characteristics of the NS atmosphere, and based on these to propose a service signal that simultaneously meets the requirements of communication, navigation, and TT&C services, as well as the modulation scheme it carries. This is aimed at improving service efficiency, simplifying the design complexity of the receiver, enhancing the spectrum utilization of the signal, cutting costs, and addressing the current communication, navigation, and TT&C task requirements for hypersonic vehicles entering the atmosphere. The structure of this paper is arranged as follows. In Section 2, the basic considerations for the integrated frequency selection of communication, navigation, and TT&C in the NS will be introduced. Section 3 will focus on analyzing the characteristics of the NS atmospheric environment and conduct a simulation analysis of the influencing factors that affect the integration of NS communication, navigation, and TT&C. On the basis of Section 3, in Section 4, in response to the requirements of the integrated channels for ICNT in the NS and providing the simulation analysis of the spectral utilization rate of typical signals and the performance of typical integrated signals, then we present our general considerations and reference solutions for the integrated modulation technology. The last section presents our research conclusions and points out the future research directions.
* The proposed CDM dynamically learns the variance during the training process, in contrast to diffusion models that learn through a fixed variance. This flexibility enables the model to optimally adjust to different modulation schemes and diverse SNR conditions, enhancing the overall training effectiveness and the rapidity of image reconstruction. * A performance analysis is conducted on the proposed CDM under diverse modulation schemes and SNR conditions. From the simulation results, it is confirmed that the proposed CDM with learnable variance (CDM-LV) is effective in reconstructing OFDM-based images from received signals in highly deteriorated environments. The remainder of this paper is organized as follows. Section 2 provides an overview of the OFDM technique and diffusion model. Section 3 details the proposed CDM, focusing on the integration of diffusion models in the context of OFDM-based image reconstruction. Section 4 presents the findings of the experiments conducted to evaluate the proposed approach. Finally, Section 5 summarizes the main findings and outlines potential directions for future research.
# Detection of Precipitation and Fog Using Machine Learning on Backscatter Data from Lidar Ceilometer
## 4 Weather Detection
### Under-Sampling

Table 10: Contingency table for prediction of a binary event. The numbers of occurrences in each category are denoted by \(a\), \(b\), \(c\), and \(d\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Under-Sampling Ratio**} & **Accuracy** & **Precision** & **FAR** & **Recall (POD)** & **F1 Score** \\ \hline
1:1 & 0.8708 & 0.6468 & 0.3532 & 0.2170 & 0.3250 \\
1:2 & 0.9025 & 0.5172 & 0.4828 & 0.2507 & **0.3377** \\
1:3 & 0.9183 & 0.4327 & 0.5673 & 0.2766 & 0.3374 \\
1:4 & 0.9277 & 0.3710 & 0.6290 & 0.2979 & 0.3305 \\
1:5 & 0.9335 & 0.3068 & 0.6932 & 0.3078 & 0.3073 \\
1:6 & 0.9380 & 0.2534 & 0.7466 & 0.3182 & 0.2821 \\
1:7 & 0.9420 & 0.2093 & 0.7907 & 0.3346 & 0.2575 \\ \hline Rand & 0.5000 & 0.0659 & 0.9341 & 0.5000 & 0.1165 \\ W-rand & 0.8769 & 0.0659 & 0.9341 & 0.0659 & 0.0659 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results of precipitation detection according to the under-sampling ratio. The bold number is the best result. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Under-Sampling Ratio**} & **Accuracy** & **Precision** & **FAR** & **Recall (POD)** & **F1 Score** \\ \hline
1:1 & 0.9472 & 0.2308 & 0.7692 & 0.0593 & 0.0944 \\
1:2 & 0.9782 & 0.0957 & 0.9043 & 0.0941 & **0.0949** \\
1:3 & 0.9844 & 0.0534 & 0.9466 & 0.1291 & 0.0756 \\
1:4 & 0.9865 & 0.0353 & 0.9647 & 0.1717 & 0.0586 \\
1:5 & 0.9872 & 0.0283 & 0.9717 &
# Reducing the False Negative Rate in Deep Learning Based Network Intrusion Detection Systems
## 4 Materials and Methods
### Development Tools  
The data preparation, model implementation, training, testing, and evaluation were all done in _Python_ using the following libraries:  
* NumPy.This is a _Python_ library which provides support for working with large multi-dimensional arrays. It allows the user to perform different mathematical operations on such arrays and it guarantees efficient calculations [42].
* Pandas. Pandas is a _Python_ library used for data analysis and manipulation. It provides support for manipulating numerical tables and time series [43].
* Matplotlib. This is a _Python_ library that provides support for data visualization. It is used to create static, animated and interactive graphs and other visualizations [44].
* Scikit-learn. This is a machine learning _Python_ library used for predictive analysis. It is built on _NumPy_, _SciPy_ and _Matplotlib_ and it can provide features for classification, regression, model selection, clustering, preprocessing and so on. Another name for it is sklearn [45].
* Tensorflow. This is a _Python_ library for machine learning. It provides features for building and training machine learning models and it allows users to create large scale neural networks with many layers [46].
* Keras. This is a _Python_ library which provides an interface for artificial neural networks. It is built on top of _Tensorflow_ and it acts as a frontend for it [47].
* Jupyter notebook. This is an interactive computational environment which allows the user to edit the code live, create equations, visualizations, and much more. It is practical for research because it allows the researcher to combine code, output, explanations, and multimedia resources in one document [48].
* PyCaret. This is an open-source _Python_ library used for automation of the machine learning processes. It gives the user many options which include automatic data preparation, automatic model construction, training of the models, and evaluation and comparison of the models [49]. For this experiment, _PyCaret_ was used to automate the data preparation and feature selection process.  
Figure 3: A graphical representation of the DNN architecture.  
All the experiments were conducted on a HP Pavilion Power laptop with the Intel(R) Core(TM) i7-7/00 HQ CPU @ 2.80 GHz processor. The rest of the hardware specifications of the laptop used for the experiment can be seen in Table 5.
## 3 Results and Discussion  
### Caffeine Levels in Milk  
Milk caffeine levels were measured by LC/MS for eight goats on LD and HD diets during the five weeks of treatment, and the results are reported in Figure 1 and Table 1.  
Figure 1: Milk caffeine mean concentrations (SD over four samples are shown as error bars) across the five weeks of experimentation. Within LD and HD groups, week means were not statistically different at \(p<0.001\). Caffeine levels were not detectable in the control samples.  
Figure 1 shows, as a column plot, the mean \(\pm\) SD concentrations of caffeine (\(\mu\)g/100 mL of milk) for each week of treatment for the HD and LD milk sample groups. As can be seen, the weekly levels of caffeine were very variable (high SD). The overall mean concentration of caffeine in milk was significantly (\(p\) < 0.001) higher in the high-dose group when compared to the low-dose group; a maximum value of 103 \(\mu\)g/100 mL was detected. Within the same group, the mean levels of each week were not statistically different (\(p\) < 0.001), suggesting that this alkaloid did not accumulate over time. In this regard, [PERSON] et al. [26] showed that following the consumption of one cup of espresso coffee (containing 80 mg of caffeine), after 24 h the caffeine concentration in breast milk was undetectable.  
The means of milk caffeine carryover after 12 h from the last administration for HD and LD groups are reported in Table 1. To calculate the caffeine carryover (caffeine excreted in milk/caffeine in the diet), the milk yield was registered (2050 \(\pm\) 481, 2047 \(\pm\) 337, and 2215 \(\pm\) 301 g for the C, LD, and HD groups, respectively, \(p\) > 0.05). The carryover, referred to as 12 h after the last SCG dose was offered, was calculated as approximately 3% for both HD and LD groups (\(p\) > 0.05). Weeks' and animals' random effects on the mean levels were also taken into account, as reported in Table 1, as the results show no influence was deemed significant (\(p\) > 0.05). To the best of our knowledge, there are no data available regarding the excretion of caffeine or other methylxanthines in ruminant milk. When feeding lactating ruminants with industrial by-products, besides the direct effects on animal wellness and productivity, the indirect effects on the quality of dairy products for human's nutrition should also be taken into consideration. In the last few decades, goat dairy products have gained more attention because of their potential health benefits for the elderly and infants [27]. Therefore, the presence of potential biologically active metabolites, such as caffeine, in caprine dairy products must be studied.
# Radar Error Correction Method Based on Improved Sparrow Search Algorithm
## 3 Improved Sparrow Search Algorithm
### Improved Sparrow Search Algorithm
#### 3.2.2 Producer-Scrounger Adaptive Adjustment Strategy  
In the sparrow search mechanism, producers and scroungers perform global exploration and local search tasks, respectively. In the early iteration, in order to expand the search scope, the proportion of producers in the population should be increased, and the number of scroungers should be appropriately increased to improve the convergence accuracy and speed of the algorithm when switching to the mode dominated by the local precise search. Therefore, the adaptive adjustment formula for the number of producers and scroungers is built as follows:  
\[r=b(\tan(-\frac{\pi\cdot iter}{4\text{iter}_{\text{max}}}+\frac{\pi}{4})-l\cdot \text{rand}(0,1)) \tag{23}\]  
\[pNum=rn \tag{24}\]  
\[fNum=(1-r)n \tag{25}\]  
where \(r\) is the nonlinear proportional coefficient used to adaptively control the number of producers and the number of scroungers, \(pNum\) is the number of producers, and \(fNum\) is the number of scroungers. \(b\) is a constant coefficient, and \(l\) is the disturbance deviation factor.
## 2 Materials and Methods
### Continuous Approach: Optimal Control
#### 2.2.5 Time Complexity  
The run time of the Newton-KKT solver is determined by the number of steps and the cost of each step. The computational effort of a Newton step is dominated by the cost of solving the linear equation (8). Due to the ODE structure, \(L_{h}^{\prime\prime}(\chi)\) is an arrow-shaped matrix with band width independent of \(\delta\tau\). Assuming quasi-uniform meshes, i.e., there is a generic constant \(C>0\) such that \(\tau_{i+1}-\tau_{i}\geq C\delta\tau\), this structure allows for an efficient solution in \(\mathcal{O}(\delta\tau^{-1})\) time using direct band solvers.  
Starting sufficiently close to the solution, say \(\omega\|\chi_{0}-\chi_{*}\|<1\), allows to bound the truncation error by linear convergence as  
\[\|\chi_{k}-\chi_{*}\|\leq(\omega\|\chi_{0}-\chi_{*}\|)^{k}\|\chi_{0}-\chi_{*}\|,\]  
which is of course rather pessimistic. A tolerance of \(\|\chi_{k}-\chi_{*}\|\leq\mathcal{O}(\delta\tau^{2})\) to match the discretization error is therefore reached after at most \(\mathcal{O}(\log\delta\tau/\log(\omega\|\chi_{0}-\chi_{*}\|))\) iterations. Thus, the overall complexity in terms of mesh width \(\delta\tau\) is  
\[R_{\mathrm{C}}=\mathcal{O}\bigg{(}\delta\tau^{-1}\frac{\log\delta\tau}{\log( \omega\|\chi_{0}-\chi_{*}\|)}\bigg{)}. \tag{10}\]  
**Remark 1**.: _If an inexact [PERSON] method based on a geometrically refined sequence of meshes with mesh width \(\delta\tau_{k}=\beta^{k}l\) for some \(l\gg\delta\tau\) and \(\beta<1\) is used, the number of iterations is determined by the linear convergence of the collocation discretization, while the truncation error is quickly diminished by the quadratic convergence of [PERSON]'s method [37]. Since the effort per Newton step grows geometrically to its final value, the complexity reduces to_  
\[R_{\mathrm{C}}=\mathcal{O}(\delta\tau^{-1}) \tag{11}\]  
_provided the initial error is sufficiently small, i.e., \(\omega\|\chi_{0}-\chi_{*}\|\leq\beta^{2}\)._
## 1 Introduction
This paper aims to clearly explain the core principles of the algorithm, provide a comprehensive overview of SSA-DBSCAN, conduct experimental analyses, and present conclusions. The contributions of this paper are as follows:  
1. Combining Sparrow Search Algorithm with DBSCAN to improve the efficiency of the latter. 2. The proposed algorithm underwent testing on five artificial datasets, seven real-world datasets, and five real images, showing strong performance with low- and medium-dimensional real data. 3. Improved the parameter selection process of DBSCAN algorithm. 4. Reducing the loss of flexibility in the current adaptive DBSCAN method helps to effectively maintain clustering quality. The remainder of this paper is structured as follows. Section 2 presents the fundamental principles and operational procedures of both the DBSCAN algorithm and the Sparrow Search Algorithm (SSA). Section 3 elaborates on the design and implementation of the proposed SSA-DBSCAN approach. In Section 4, an experimental evaluation is described, focusing on the performance of SSA-DBSCAN across synthetic datasets, benchmark datasets, and real-world image data. The outcomes are compared with those of several widely used clustering techniques, followed by an in-depth discussion. Section 5 offers a summary of the experimental findings along with a sensitivity analysis. Lastly, Section 6 concludes the study and outlines potential directions for future work.
## 5 Summary and Discussion
### Challenges and Limitations
#### 5.2.3 IoT Integration Challenges  
* Data security and privacy--Port cranes are often critical for national security and economic interests. Ensuring the security and privacy of the data collected from the sensors is paramount. The implementation of robust encryption protocols, secure authentication mechanisms, and access control measures can help protect data from unauthorized access and cyber threats.
* Data management and storage--The large volume of data generated by vibration and AE sensors requires efficient data management and storage solutions. Cloud storage offers scalable and cost-effective storage options; however, it is essential to implement proper data organization and retrieval mechanisms to ensure quick access to relevant data.
* Network connectivity and reliability--Port environments pose challenges to network connectivity. Ensuring reliable and consistent network connectivity is crucial for transmitting sensor data. Using redundant network connections, satellite communication, and edge computing can help mitigate connectivity issues.
* Sensor calibration and maintenance--Vibration and AE sensors require regular calibration and maintenance to ensure accurate and reliable data collection. Developing automated calibration procedures and implementing remote maintenance capabilities can help reduce the need for manual intervention and minimize downtime.
* Data analysis and interpretation--The analysis and interpretation of data from vibration and AE sensors can be complex because of the high volume and variability of the data. Advanced data analysis techniques, such as machine learning and artificial intelligence, can help automate the analysis process and improve the accuracy of structural issues detection.
## 4 Simulation Results and Analysis
### Performance of the Joint Frequency-Power Allocation Algorithm  
In this section, we evaluate the performance of the proposed joint frequency-power optimization algorithm through simulation. For power allocation, three baseline schemes are used for comparison: (1) uniform power allocation across both beams and users, (2) uniform power allocation across beams only, and (3) uniform power allocation across users only. The simulation results comparing all four schemes are presented in Figure 4.  
The simulation results indicate that optimizing power allocation either across beams or within beams can significantly enhance system throughput. Among the four evaluated schemes, Algorithm C, which performs joint power allocation for both satellite beams and users, achieves the highest performance gain. It improves the total system throughput by approximately 7.2% to 20% compared to the other uniform power allocation strategies.  
Figure 3: Performance of globally optimized beam–slot assignment algorithm. (**a**) Beam–slot distribution before optimization; (**b**) beam–slot distribution after optimization.
## 3 Pharmacological Properties of GP
### Lipid-Regulating Effect  
#### 3.3.1 Effect on Hyperlipidemia  
A large number of studies had shown that GP had the effect of lowering blood lipid and was commonly used together with other traditional Chinese medicines or combined with Western medicines to treat hyperlipidemia. GPS was the major component that played an important role in lowering blood lipid levels. It was found that the nucleus structure of GPS was very similar to that of endogenous bile acids, which could activate the farnesoid X receptor in the liver, thus up-regulating the key enzymes CYP8B1 and CYP7A1 in bile acid synthesis, promoting the synthesis and secretion of bile acids, promoting lipid metabolism, and lowering blood lipid [64]. [PERSON] [65] studied the lipid-lowering effect of gypenoside SL-1 and SL-2 isolated from GP on HepG2 cells in vitro. The results showed that SL-1 and SL-2 (10, 50, and 100 \(\upmu\)g/mL) could decrease the content of TC, TG, and low-density lipoprotein (LDL) and increase the content of HDL, thus reducing lipid accumulation in HepG2 cells incubated with oleic acid. Currently, the first-line drug used for the treatment of hyperlipidemia was statins. However, its lipid-lowering effect could not reach the target proposed by the National Institutes of Health of the United States [66]. That was because the expression of proproticen convertase subtilisin/kexin 9 (PCSK9) while lowering lipids was enhanced, which further induced the increased low-density lipoprotein receptor (LDLR) degradation, thus inhibiting the lipid-lowering effect [67]. A number of clinical studies had reported that statin therapy might cause the increase in transaminase in the body while lowering lipid levels, thus affecting liver function [66]. Studies had demonstrated that GPS could significantly lower blood lipids, and when combined with simvastatin, the effect of lowering lipid levels was improved by suppressing the increased expression of PCSK9 and reducing the degradation of LDLR. In addition, the increase in serum transaminase induced by simvastatin was reversed, and liver function was improved [67; 68]. [PERSON] et al. isolated and identified ten novel saponins named yunnangypenosides (1-10) and a known one from GP. Furthermore, these compounds could significantly reduce lipid levels except for those of 2 [69].
# On the Evaluation of Complex Networks Designs for an Energy-Efficient IP/WDM Core Network
## 3 Complex Network Designs  
In this section, we review the complex networks, describe their properties, and justify recent interest by researchers for the consideration of such topologies in networks. The main models, covering random graphs, small-world network, and SFNs shall be described in this section.  
Complex networks are found to be energy-efficient, robust, and fault-tolerant SFN physical topologies that display stability and a high degree of tolerance against physical impairments and link or node failures. In the occurrence of dynamic changes such as link failures or the removal of links from routing tables, these networks sustain and produce a new network that is still highly connected and able to deliver information and maintain delay with acceptable predefined performance measures. With the rich connectivity of the complex networks, the power consumption of the network can be reduced by grooming traffic on fewer links and putting underutilized links on sleep mode.  
The main models of the complex networks, covering random graphs, small-world networks, and SFNs are briefly described in the following.