esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Detection of Resilient Navigation System Based on GNSS Pseudo-Range Measurement
## 5 Conclusions  
To improve the fault detection performance of factor graph navigation systems under complex environments, a fault detection method is applied. It provides the capability of satellite pseudo-range fault location, which can detect the fault pseudo-range measurements and improve the positioning accuracy of the resilient integrated navigation system after isolation. The conclusions can be summarized as follows:  
1. The factor graph navigation algorithm has been widely used because it can effectively solve the problem of sensor asynchrony in navigation information fusion and realize the plug-and-play configuration of multiple sensors.
2. Based on the Chi-square testing and MSS method, a new fault detection method based on a factor graph framework is designed and constructed through the INS measurements and GNSS pseudo-range. The proposed method can locate faulty pseudo-range measurements.
3. The simulation and open-sourcing data experimental results show that the proposed method has a better performance by improving fault detection under complex interference. Compared with the Chi-square fault detection method, the navigation performance is improved by more than 40%.  
Conceptualization, K.S. and Q.Z.; methodology, K.S.; software, K.S.; validation, K.S., Q.Z. and S.W.; formal analysis, S.W.; investigation, S.W.; resources, J.L.; data curation, S.W.; writing--original draft preparation, K.S.; writing--review and editing, Q.Z.; visualization, K.S.; supervision, Q.Z.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant 61533008, 61603181), the Fundamental Research Funds for the Central Universities (No. NS2018021). This research was also supported by the Fundamental Research Funds for the Central Universities, and Engineering Research Center of Autonomous Control Technology of aircraft, Ministry of Education.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank [PERSON] in the Hong Kong Polytechnic University for providing the datasets for this study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Random Access for Underwater Acoustic Cellular Systems
## 3 RAP Design  
In this section, two different types of RAPs for underwater acoustic cellular systems are proposed after reviewing the conventional technique used in LTE cellular systems. The design of an LTE-based RAP is described first owing to its similarity to our study and simplicity at the receiver side (low computational complexity for RAP detection).
###### Abstract  
Various hydrogeological problems like groundwater inflow, water table drawdown, and water pressure redistribution may be encountered in the construction of hydraulic projects. How to accurately predict the occurrence of groundwater inflow and assess the drainage effect during construction are still challenging problems for engineering designers. Taking the Jinzhai pumped storage power station (JPSPS) of China as an example, this paper aims to use different methods to calculate the water inflow rates of an underground powerhouse and evaluate the drainage effect caused by tunnel inflow during construction. The methods consist of the analytical formulas, the site groundwater rating (SGR) method, and the Signorini type variational inequality formulation. The results show that the analytical methods considering stable water table may overestimate the water inflow rates of caverns in drained conditions, whereas the SGR method with available hydro-geological parameters obtains a qualitative hazard assessment in the preliminary phase. The numerical solutions provide more precise and reliable values of groundwater inflow considering complex geological structures and seepage control measures. Moreover, the drainage effects, including a seepage-free surface, pore water pressure redistribution, and hydraulic gradient, have been accurately evaluated using various numerical synthetic cases. Specifically, the faults intersecting on underground caverns and drainage structures significantly change the groundwater flow regime around caverns. This comparative study can not only exactly identify the capabilities of the methods for cavern inflow in drained conditions, but also can comprehensively evaluate the drainage effect during cavern construction.  
groundwater inflow; drainage effect; numerical modeling; comparative study +
Footnote †: journal: Applied sciences  
0000-0001-9912-3399/app14199123
## 4 Discussion
### Experimental Analysis
In most existing AUV path planning methods, the main focus is on researching algorithms for obstacle avoidance, anti-current interference, and path control [7; 8; 9]. In research related to AUVs equipped with side-scan sonar [10; 11], the emphasis is more on target detection, which differs from the purpose of using AUVs for seabed terrain detection in this article. Therefore, the proposed method in this article is not directly comparable in quantitative indicators. However, qualitatively comparing and analyzing, the survey line planning method proposed in this article is more suitable for AUVs equipped with side-scan sonar to conduct seabed terrain detection operations. From the perspective of algorithm principles, the survey line planning method proposed in this article, especially the fixed-point mode algorithm principle, is simpler than path planning methods used for target detection. Due to its simplicity, this method's principle may have lower applicability to more complex environments and path planning. Overall, however, the survey line planning method proposed in this article achieved a result with good applicability.
# Enhanced Trajectory Tracking via Disturbance-Observer-Based Modified Sliding Mode Control
## 2 Control Design  
In this section, the control theory is elaborated in detail. The purpose of the proposed method is to achieve the desired state even in the presence of perturbations. The concept of the proposed method can be observed in Figure 1. The goal of the designed algorithm is to effectively reach the desired state despite the presence of perturbations. The extended-state observer (ESO) is used to estimate both the system state and the perturbation. The estimated-perturbation information is then utilized in conjunction with the PIDSMC (proportional-integral-derivative sliding mode control) input to achieve the desired state in a satisfactory manner. In Section 2.1, a saturation-function-based ESO is designed for general second-order systems. In Section 2.2, the ESO-based PIDSMC is developed and discussed in detail. Section 2.2 describes the performance characteristics of the proposed algorithm.
## Appendix A Optimization Method
### Modulation Efficiency  
Modulation efficiency and phase error (\(\Delta\theta\)) are calculated using the following equations to assess the EOM performance:  
\[H\Big{(}\omega_{rf}\Big{)} = \frac{Z_{in}}{Z_{in}+Z_{L}}\frac{1}{e^{\gamma l}+Re^{-\gamma l}}\times \tag{12a}\] \[\left[\frac{Sinh\Big{(}\frac{A\dagger}{2}\Big{)}e^{-\frac{A \dagger}{2}}}{\frac{A}{2}}+R\frac{Sinh\Big{(}\frac{B\dagger}{2}\Big{)}e^{- \frac{B\dagger}{2}}}{\frac{B}{2}}\right]\] \[A=-\alpha_{rf}-j\Big{(}\beta_{rf}-\beta_{op}\Big{)}\] (12b) \[B=\alpha_{rf}-j\Big{(}\beta_{rf}+\beta_{op}\Big{)}\] (12c) \[\Delta\theta=\theta-\frac{\pi n_{e}^{3}r_{33}\tau LV_{\pi}}{\lambda_{o}}\] where \[\theta=\frac{\pi n_{e}^{3}r_{33}\tau LV_{\pi}}{\lambda_{o}}H\Big{(}\omega_{ rf}\Big{)} \tag{12d}\]  
Modulation efficiency (m) is thus given by  
\[m=\frac{H\Big{(}\omega_{rf}\Big{)}}{H(0)} \tag{12e}\]
# Automatic Parsing and Utilization of System Log Features in Log Analysis: A Survey
## 3 Feature Extraction and Utilization
### Comparison of Feature Utilization
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Year**} & \multicolumn{6}{c}{**Features**} & \multicolumn{6}{c}{**HDFS**} \\ \cline{3-10}  & & **Count** & **Time** & **Event** & **Index** & **Sequence** & **Parameter** & & \multicolumn{1}{l}{**Precision**} & \multicolumn{1}{l}{_Recall_} & \multicolumn{1}{l}{\(\mathbf{F_{i}}\)} \\ \hline LogCluster & 2016 & ✓ & & & & & & & Cluster & 0.99 & 0.77 & 0.87 \\ SVM etc. & 2017 & ✓ & & & & & & & SVM etc. & - & - & - \\ IM etc. & 2017 & ✓ & & & & & & & IM etc. & - & - & - \\ DeepLog & 2017 & & ✓ & & ✓ & & ✓ & & LSTM & 0.93 & 0.94 & 0.93 \\ LogCNN & 2018 & & & & ✓ & & & & CNN & - & - & - \\ CausalConvLSTM & 2019 & & & & ✓ & & & & & CNN/LSTM & 0.9 & 1 & 0.94 \\ LogAnomaly & 2019 & ✓ & & & ✓ & & & & LSTM & 0.96 & 0.94 & 0.95 \\ LogGAN & 2020 & & ✓ & & ✓ & & & & GAN/LSTM & 1 & 0.36 & 0.53 \\ LogRobust & 2020 & & & ✓ & & & & & ✓ & Bi-LSTM & 0.98 & 1 & 0.99 \\ SwissLog & 2020 & & ✓ & ✓ & & & & ✓ & BERT/LSTM & 0.97 & 1 & 0.99 \\ HitAnomaly & 2020 & & & ✓ & & & ✓ & ✓ & Transformer & 1 & 0.97 & 0.98 \\ LogEvent2 vec & 2020 & & & & ✓ & & & & RF etc. & - & - & - \\ ConAnomaly & 2021 & & & ✓ & & & & ✓ & LSTM & 1 & 0.98 & 0.98 \\ LogBert & 2021 & & & ✓ & & & & ✓ & BERT & 0.87 & 0.78 & 0.82 \\ LAnoBERT & 2021 & & & ✓ & & & & ✓ & BERT & - & - & 0.96 \\ LogAD & 2021 & ✓ & & ✓ & ✓ & & ✓ & ✓ & LSTM etc. & - & - & - \\ KNN-Based & 2021 & & & ✓ & & & & ✓ & KNN & 0.97 & 0.99 & 0.98 \\ LogDP & 2021 & ✓ & & & & & & ✓ & MLP & 0.98 & 0.99 & 0.99 \\ Sprelog & 2021 & & & ✓ & & & & ✓ & RoBERTa & - & - & - \\ PILEog & 2021 & & & ✓ & & & & ✓ & GRU & 0.95 & 0.96 & 0.96 \\ AllInfoLog & 2022 & & ✓ & ✓ & & ✓ & & ✓ & RoBERTa/Bi- & 0.99 & 0.99 & 0.99 \\ DeepTraLog & 2022 & & & ✓ & & & & ✓ & SVDD/GGNNs & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Feature utilization, robustness, and effect comparison of log anomaly detection methods. of real-world datasets. In the system log, the number of normal log sequences was far greater than the number of abnormal log sequences. This sample imbalance problem greatly affected the accuracy of the anomaly detection. [PERSON] et al. [68] proposed to alleviate the class imbalance of log datasets by using generative adversarial networks. In their research, the feature extraction link also used log index features, captured features through the generator, generated credible log sample data, and finally used the discriminator to identify whether the sample was generated by the generator so as to improve the sample quality of the generator. However, this method is less robust on real-world datasets. [PERSON] et al. [69] proposed a semi-supervised anomaly detection method called PLELog, which uses probabilistic label estimation to reduce the anomaly detection error caused by uneven sample labels.
## 3 Verification of Proposed Model
### Service Life of Cracked Concrete Considering Climate Change
The proposed model considered the influences of material properties (mixing proportions and curing periods), crack characteristics (crack width and crack interspace) and environmental conditions (CO\({}_{2}\) concentration, temperatures and relative humidity) on carbonation resistance and the service life of concrete structures. The calculated results regarding corrosion initiation time and service life can be combined with structural health monitoring technology for the design and maintenance of concrete structures [18; 19]. However, the proposed model has its limitations. First, the CO\({}_{2}\) diffusivity in the cracked zone was not analyzed in detail. The crack geometry shape such as equal width cracks or tapered cracks were not taken into account [5]. Second, the interaction between moisture diffusion, thermal diffusion, carbonation reaction and CO\({}_{2}\) diffusion were also not taken into account [20]. In this study, only a simplified equation (Equation (4)) was employed to calculate the carbonation depth of concrete. Third, the defined service life in this study was too conservative. The corrosion propagation period should be included in the service life of concrete structures [21]. In addition, the effect of other atmospheric variables on carbonation such as wind pressure, especially at the surface of concrete buildings, should also be considered in further studies [22]. By combining the penetration-reaction carbonation model [22] with the Monte Carlo method, the influence of wind pressure on carbonation service life can be found. Figure 19: Relative service life of cracked concrete-RCP 8.5 upper 90% confidence interval, time slice 2050.
## 4 Discussion
It is our experience, and this study shows, that conventional compressive dressings tend to loosen and slide after prolonged physiotherapy, making the application of new dressings after such procedures necessary. In contrast, and in accordance with their findings, the PICO system did not require renewal after such procedures and showed no signs of loss of pressure, thus reducing the necessity of frequent dressing changes. These findings make the application of such a system generalizable to anatomical areas with less range of motion. A study performed by [PERSON] et al. on the effect of NPWT using the VAC Therapy (Kinetic Concepts Inc., San Antonio, TX, USA) in the immediate postoperative period after total knee arthroplasty had to be cut short due to the occurrence of blister formation in 63% of the study group. In their study, blisters were observed at the skin-sponge boundary and attributed to an increase in friction generated during postoperative exercises. The authors encouraged further research in the reduction of blister formation while utilizing NPWT [36]. Besides its portability and ease of application, PICO was chosen as it also promises to distribute pressure evenly over the applied surface, thus decreasing lateral and shear stress over sutures, a possible mechanism that could lead to the occurrence of blisters. Blister formation was not a complication observed in our trial. Furthermore, none of the 34 participants complained of an increase in pain regarding the application of the system, during dressing changes, or throughout the duration of the system's application. Patients could shower with the system in place, and mobility was not impaired. Limitations to this study are its small sample size and the fact that the rate of wound healing complications or PJI after TKA is generally low. Additionally, lacking standardized authenticated methods of assessment, wound healing criteria were based on semi-quantitative and semi-qualitative observation. Furthermore, observed complications such as deep hematoma were not ascribed as dependent on the chosen dressing in either group but because of the performed surgery. PICO dressings can be applied on a wound for a maximum of seven days without dressing change. To allow for better wound observation, dressings were changed every three days so that only definite statements for this time frame can be made.
## 2 Numerical Simulations and Wind Tunnel Tests
### Turbulence Model
#### 2.2.1 RANS Method  
RANS is the most widely used method for numerical simulation due to its short computational time and low computational cost. The core idea is to avoid solving the transient Navier-Stokes equations directly by time-homogenizing them using the statistical theory of turbulence, thus reducing computational effort. However, this process leads to the appearance of Reynolds stress, causing the system of equations to be unclosed. Therefore, the turbulence model is needed to enable the system of equations to be solved. Here, three RANS turbulence models, RNG, REA, and SST, are selected for the numerical simulation of wind fields over the hill based on the impinging jet model. The following is a brief description of these three turbulence models.  
The RNG model was proposed by [PERSON] and [PERSON] [32]. It extends the standard \(k\)-\(\epsilon\) model by using a renormalization group approach and has the following main features: an additional term \(R_{\epsilon}\) is added to the transport equation to improve the accuracy of fast strain flow; an analytically derived differential formulation of the effective viscosity is added to account for low Reynolds number effects. These characteristics make the results of the RNG model more accurate than those of the standard model, and RNG is more widely used.  
The REA model was proposed by [PERSON] et al. [33]. Based on the standard \(k\)-\(\varepsilon\) model, the REA model adds alternative formulas for turbulent viscosity and corrects the transport equation for the dissipation rate \(\varepsilon\). This allows for the accurate prediction of flow field information in simulations of walls with large curvature or inverse pressure gradient and flow separation.  
The SST model was improved from the standard \(k\)-\(\omega\) model by [PERSON] [34]. It revises the definition of turbulent viscosity and mainly considers the transport of the main turbulent shear stress. It can solve the standard turbulent kinetic energy \(k\) equation and the equation of a specific turbulence rate \(\omega\). The SST model combines the accuracy of the standard \(k\)-\(\omega\) model in the near-wall region with the independence of the standard \(k\)-\(\varepsilon\) model in inlet-free turbulence. As a result, this model produces more accurate results than both the standard \(k\)-\(\omega\) and standard \(k\)-\(\varepsilon\) models.  
The RANS method is based on the statistical averaging of flow by the turbulence model. However, it cannot capture the pulsatility of turbulence or accurately simulate high-intensity linear flow. Additionally, its turbulent kinetic energy pulsation spectrum cannot be resolved.
## 5 Numerical Results  
The computation processing time for the 1280 s simulation was approximately 5 h, with the model being run on a workstation equipped with an Intel Core i9-13900K processor, 36M Cache, up to 5.80 GHz, and 32 GB RAM, manufactured by DELL Romania and purchased in Bucharest, Romania. The results obtained from the simulation will be presented next, collected through monitoring points and planes for temperature, velocity, and visibility. The monitoring points are located as indicated in Figures 3 and 4 and gather data on temperature, speed, and concentrations of CO and CO\({}_{2}\) in the air. The monitoring planes were positioned longitudinally and transversely, symmetrically sectioning the test space, as well as horizontally at heights of 1, 1.75, and 2.9 m.  
Figure 10 highlights the dynamics of the simulated phenomenon and its impact on space. It captures intense burning with flames reaching window level, generating high temperatures and substantial smoke emission. The interdependence between fire and ventilation dictates the speed of propagation and direction of smoke, thereby affecting visibility and the safety of individuals.
# An Efficient Imaging Method for Medium-Earth-Orbit Multichannel SAR-GMTI Systems
## 2 Signal Modelling
### Range Equation Modelling
In this paper, the bold letters represent vectors, and the letters of italic fonts represent the norms of vectors. To facilitate the design of the GMTI method, the target's range history is generally approximated by a Taylor series expansion around the time that the beam centerline crosses the target (i.e., \(t_{a}=0\)). In the case of MEO SAR, because of the obvious curve of the trajectory, a third-order Taylor series expansion should be adopted. By exploiting the vector projection and matrix differential, the cubic-approximated range equation for the multichannel system is given by [20]  
\[R_{n}(t_{a})\approx R_{0}+(l_{1}+(n-1)d\cdot\alpha)t_{a}+l_{2}t_{a}^{2}+l_{3} t_{a}^{3}, \tag{5}\]  
with  
\[R_{0}\approx\left\lvert\mathbf{r_{t}}(0)-\mathbf{r_{s}}(0)\right\rvert \tag{6}\]  
\[l_{1}\approx v_{tr} \tag{7}\]  
\[l_{2}\approx\frac{v_{ft}^{2}-2v_{ta}v_{s0}+v_{s0}^{2}-v_{tr}^{2}}{2R_{0}}- \frac{a_{sr}}{2}+\frac{a_{tr}}{2} \tag{8}\]  
\[l_{3}\approx\frac{3a_{sat}v_{s0}-j_{sr}R_{0}}{6R_{0}}-\frac{3a_{ta}v_{s0}}{6R _{0}} \tag{9}\]  
\[\alpha=\frac{v_{ta}-v_{s0}}{R_{0}}+\frac{a_{sr}}{v_{s0}}, \tag{10}\]  
where \(v_{s0}=\left\lvert\mathbf{v_{s}}(0)\right\rvert\) and \(v_{t0}=\left\lvert\mathbf{v_{t}}(0)\right\rvert\) are, respectively, the velocities of the radar and target at \(t_{a}=0\). In addition, \(v_{tr}\) and \(a_{tr}\) represent the target's radial velocity and acceleration; \(v_{sr}\), \(a_{sr}\) and \(j_{sr}\) are, respectively, the radial projections of the radar's velocity, acceleration and jerk; \(v_{ta}\) and \(a_{ta}\) define the target's along-track velocity and acceleration; \(a_{sa}\) is the radar's acceleration. They can be expressed as [20]  
\[v_{tr}=v_{t}(0)\frac{\left[\mathbf{r_{t}}(0)-\mathbf{r_{s}}(0)\right]^{T}}{R_{0}}, \tag{11}\]  
\[v_{sr}=v_{s}(0)\frac{\left[\mathbf{r_{t}}(0)-\mathbf{r_{s}}(0)\right]^{T}}{R_{0}}, \tag{12}\]  
Figure 2: MEO multichannel SAR geometry. \[R_{n}(t_{a})\approx R_{0}+(l_{1}+(n-1)d\cdot\alpha)t_{a}+l_{2}t_{a}^{2}+l_{3} t_{a}^{3}, \tag{5}\]0 \[R_{n}(t_{a})\approx R_{0}+(l_{1}+(n-1)d\cdot\alpha)t_{a}+l_{2}t_{a}^{2}+l_{3} t_{a}^{3}, \tag{5}\]1 (14) \[R_{n}(t_{a})\approx R_{0}+(l_{1}+(n-1)d\cdot\alpha)t_{a}+l_{2}t_{a}^{2}+l_{3} t_{a}^{3}, \tag{5}\]2 (15) \[R_{n}(t_{a})\approx R_{0}+(l_{1}+(n-1)d\cdot\alpha)t_{a}+l_{2}t_{a}^{2}+l_{3} t_{a}^{3}, \tag{5}\]3 (16) \[R_{n}(t_{a})\approx R_{0}+(l_{1}+(n-1)d\cdot\alpha)t_{a}+l_{2}t_{a}^{2}+l_{3} t_{a}^{3}, \tag{5}\]4 (17) \[R_{n}(t_{a})\approx R_{0}+(l_{1}+(n-1)d\cdot\alpha)t_{a}+l_{2}t_{a}^{2}+l_{3} t_{a}^{3}, \tag{5}\]5  
where \(v_{s}(0)\), \(a_{s}(0)\), and \(j_{s}(0)\) are, respectively, the velocity, acceleration and jerk vectors of satellite at \(t_{a}=0\); \(v_{t}(0)\) and \(a_{t}(0)\) indicate the velocity and acceleration vectors of the target at \(t_{a}=0\).