esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Setup
### Marx Generator Output  
To isolate the effects of the gas within the spark gap, the consistency of the high-voltage pulse source is measured. An 8-stage Marx generator is used to generate the high voltage, HV, pulse in this setup. The Marx generator is charged to \(-\)38 kV and is arranged in an inverting configuration so the output pulse is positive. The Marx generator is pressurized to 304 kPa of lab air and is electrically triggered using a trigatron in the first stage electrode [27]. The output is captured using a CVD, as depicted in Figure 1.  
The output voltage waveform of the Marx generator is shown in Figure 5. The maximum output voltage, risetime, and dV/dt are calculated for every shot. The Marx generator produces a 83.5 kV, 10 ns rise time excitation (6.8 kV/ns). The standard deviations in amplitude and risetime were observed to be 3% and 8%, respectively. However, due to losses in the 2121 HV cable spool, what reaches the cable is very similar in shape to the [PERSON] generator output waveform but 10 kV lower in amplitude. All datasets in the report have similar [PERSON] generator outputs. Based on the statistical deviation of the [PERSON] generator, the breakdown electric field may vary anywhere between 3.3-16.1 kV/cm.  
Figure 4: Measured transfer functions of the Rogowski and CVD probes. The usable frequency of each probe is 600 MHz for the Rogowski coil and 2.5 GHz for the CVD.
# An Improved Circumferential Fourier Fit (CFF) Method for Blade Tip Timing Measurements
## 2 The Improved Circumferential Fourier Fit (ICFF) Method
### The Theoretical Basis of ICFF  
Assuming the blade response is of single-frequency vibration:  
\[y\ =\ A\sin(\omega t+\varphi)+d \tag{1}\]  
where \(y\) represents the vibration displacement of the blade tip, \(A\) represents the amplitude, \(\omega\) represents the angular frequency, \(t\) represents the ToA, \(\phi\) represents the phase, and \(d\) represents the direct current offset. For the synchronous vibration, there is:  
\[\omega\ =\ \text{EO}\cdot\omega_{v} \tag{2}\]  
where \(\omega_{v}\) represents the rotor rotating frequency. This is based on the BTT measurement principle, assuming that the blade rotates to the monitoring position \(\beta\) at time \(t\). The geometry relationship between the rotation angular, monitoring position, and blade vibration is shown in Figure 2. The equivalence relationship can be given by:  
\[\omega_{p}t\ =\ \int_{0}^{t}\overline{\omega}_{p}dt\ =\ 2\pi n+\beta+ \varepsilon-y/R \tag{3}\]where \(\overline{\omega}_{v}\) is the average rotating speed, and \(n\) is the number of revolution. In the Equation (3), it can be seen that the time \(t\) (that is ToA) is not only dependent on the rotating speed and monitoring position, but also on the blade vibration.  
Let:  
\[\overline{\beta}\ =\ \beta+\varepsilon-y/R \tag{4}\]  
According to Equations (1)-(4), the blade vibration displacement monitored by the probe mounted at the position \(\beta\) can be given:  
\[y\ =\ A\sin(EO\overline{\beta}+\varphi)+d. \tag{5}\]  
Equation (5) is the mathematical model for monitoring the vibration displacement of the blade tip under the influence of blade vibration that is considered. According to the literature [32], in the case of large vibration displacement or mistuning, the improved single-parameter method that considers the influence of blade vibration has higher accuracy than the traditional method. ICFF is a combination of the theory of the improved single-parameter method and TCFF.
# A Measure Based on Beamforming Power for Evaluation of Sound Field Reproduction Performance
###### Abstract  
This paper proposes a measure to evaluate sound field reproduction systems with an array of loudspeakers. The spatially-averaged squared error of the sound pressure between the desired and the reproduced field, namely the spatial error, has been widely used, which has considerable problems in two conditions. First, in non-anechoic conditions, room reflections substantially deteriorate the spatial error, although these room reflections affect human localization to a lesser degree. Second, for 2.5-dimensional reproduction of spherical waves, the spatial error increases consistently due to the difference in the amplitude decay rate, whereas the degradation of human localization performance is limited. The measure proposed in this study is based on the beamforming powers of the desired and the reproduced fields. Simulation and experimental results show that the proposed measure is less sensitive to room reflections and the amplitude decay than the spatial error, which is likely to agree better with the human perception of source localization.  
sound field reproduction; higher-order ambisonics (HOA); evaluation of reproduction performance; beamforming power; loudspeaker arrays +
Footnote †: journal: Applied Sciences
# Update: Heart Failure in Systemic Lupus Erythermatosus
## 3 Pathophysiology and Risk Factors of Heart Failure in Systemic Lupus Erythermatosus
### Myocarditis and Endocarditis  
Myocarditis is associated with chronic inflammation and fibrosis, which secondarily leads to impaired systolic function of the myocardium. Complement components, chronic inflammation, and deposition of immune complexes are involved in these processes. Antibodies: anti-Ro (SS-A), antiribonucleoprotein, and antiphospholipid antibody may also be involved. Myocarditis is observed in about 10% of SLE patients. Moreover, postmortem studies have shown the presence of myocarditis in up to 2/3 of patients [56; 57]. It predominantly affects younger patients and can range from asymptomatic to severe heart failure [8; 56; 58; 59]. A type particularly associated with SLE is Libman-Sacks endocarditis, which may cause valvular dysfunction and be another factor in the development of HF [33; 60; 61; 62].  
Myocarditis in SLE patients is a serious condition, as it can lead to severe HF and dilated cardiomyopathy [56; 63; 64]. Cases of heart failure during pregnancy and the postpartum coated by myocarditis have also been described [10; 65]. However, myocarditis and dilated cardiomyopathy are late manifestations of myocardial dysfunction and are relatively infrequent [45; 66]. In patients with HFpEF with a history of myocarditis, regional wall motion (RWM) abnormalities and significant diastolic dysfunction are observed. Thus, these parameters are sensitive markers of general myocardial dysfunction [67]. LVEF reduction after myocarditis occurs in about 1% of patients. Most patients remain in the group with preserved ejection fraction. However, diastolic dysfunction is observed in almost 90% of patients [68; 69]. Similarly, regional disturbances in the motion of the heart walls are found in the majority of patients [60]. Magnetic resonance imaging (MRI) remains the gold standard for diagnosis [70; 71; 72].
# Recent Advances of MXene-Based Electrochemical Immunosensors
## 3 Electrochemical Immunosensors Based on MXenes
### MXenes as Detection Antibody Label in Electrochemical Immunosensors
Using the hc-g-C3N4@CDs (high-crystalline graphitic carbon nitride@carbon dots) core-shell nanostructure as sensing matrix, the prepared immunosensor indicated an ultra-short detection time and a high sensitivity, with a LOD of 3.30 fg mL\({}^{-1}\). Figure 5: Schematic illustration of sandwich-type immunosensor for quantitative detection of PSA. Reprinted with permission from Ref. [59]. 2020, Elsevier. [PERSON] et al. [63] reported a sandwich-type immunosensor for ultrasensitive detection of KIM-1, a powerful biomarker of acute kidney injury (AKI) that can non-invasively reflect the kidney injury and recovery process. Specifically, PdPtBP MNPs/MXene nanocomposites were successfully synthesized using polydopamine (PDA) functionalized MXene with large surface area and the quaternary PdPtBP MNPs nanozymes (Figure 6b). Since the PdPtBP MNPs nanozymes had excellent peroxidase-like catalytic activity and could covalently bind to the antibodies, the PdPtBP MNPs/MXene nanocomposites were used as Ab\({}_{2}\) signal amplification labels. Meanwhile, the combination of highly conductive CuCl\({}_{2}\) nanowires and AuNPs was used as the sensing matrix to conjugate the Ab\({}_{1}\) onto the GCE surface. Benefiting from the high catalytic activity of PdPtBP MNPs nanozymes and the excellent conductivity of CuCl\({}_{2}\) NWs, the prepared immunosensor exhibited satisfactory performance for ultrasensitive detection of KIM-1 and can be successfully applied for the specific detection of KIM-1 in urine.
## 3 Results & Discussion
### PAHs in PM\({}_{2.5}\)
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Suburban Residential Area**} & \multicolumn{2}{c}{**BI (Background)**} & \multicolumn{1}{c}{**Seoul (Capital)**} \\ \cline{2-5}
**Unit** **ng/m\({}^{3}\)** & **Overall** & **Standard** & & & \\  & **Average** & **Deviation** & **Maximum** & **Overall Mean** & **Overall Mean** \\ \hline Phenanthrene(Phen) & 0.67 & 1.05 & 5.64 & 0.02 & 0.02 \\ Anthracene(Anth) & 0.06 & 0.08 & 0.50 & 0.00 & 0.03 \\ Fluoranthene(Fluo) & 1.33 & 1.91 & 10.59 & 0.11 & 0.18 \\ Pyrene(Pyre) & 1.09 & 1.68 & 10.13 & 0.03 & 0.10 \\ Benzo[a]anthracane(BaA) & 0.60 & 0.98 & 4.60 & 0.04 & 0.09 \\ Chrysene(Chry) & 1.85 & 2.02 & 8.16 & 0.27 & 0.43 \\ Benzo[b]fluoranthene(BbF) & 1.84 & 1.77 & 6.39 & 0.21 & 0.33 \\ Benzo[k]fluoranthene(BkF) & 0.54 & 0.51 & 2.31 & 0.14 & 0.20 \\ Benzo[e]pyrene(BeP) & 0.79 & 0.73 & 2.63 & 0.14 & 0.22 \\ Benzo[a]pyrene(BaP) & 0.37 & 0.64 & 3.11 & 0.08 & 0.13 \\ Indeno123 cdpyrene(Indeno) & 1.02 & 0.95 & 3.67 & 0.16 & 0.31 \\ Dibenz[a,h]anthracane(DBA) & 0.19 & 0.18 & 0.87 & 0.01 & 0.09 \\ Benzo[g,h,i]perylene(BghiP) & 1.30 & 1.26 & 6.42 & 0.21 & 0.39 \\ \(\Sigma\) & 11.65 & & & 1.42 & 2.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PAH concentrations in the study area and major cities in Korea. The ratio of PAHs emitted from asphalt in this study was compared with the results of previous studies. In previous studies, the ratio of Fluo/Pyre discharged from asphalt was about 1.17 and the ratio of Fluo/BeF was about 0.52 [44, 45]. In contrast, in this study, the ratio of Fluo/pyre was 1.22 and the ratio of Fluo/BeF was 0.72. These ratios are similar to those found in previous studies. The characteristics of PAHs generated by the raw materials used to make asphalt and the manufacturing process are different [46, 47]. However, comparing the results of this study with the results of previous studies indicates that the PAHs measured in this study were generated by asphalt. An itemized correlation analysis of the analyzed PAHs is shown in Figure 4. The correlations (r\({}^{2}\)) for BaA vs. BaP; BbF vs. BeP, Indeno, BeP, and BghiP; and BeP vs. Indeno, DbA, and BghiP were greater than 0.88 in all cases. This indicates that the emission source of the PAHs contained in PM\({}_{2.5}\) is the same source. In addition, changes in the PM\({}_{2.5}\) concentration did not have a significant effect on the ratio of PAH components, indicating that the concentrations of PM\({}_{2.5}\) and PAHs were proportional to each other. Based on the OC, EC, and BC measurement results, we confirmed that the emission source for PM\({}_{2.5}\) was the asphalt mix plant and that the concentrations of PM\({}_{2.5}\) emitted and PAHs were proportional to each other. In conclusion, PAHs in the study area are attributable to emissions from the asphalt mix plant. Figure 3: PAH cross-plots for the ratio of Anth/(Anth/Phen) vs. BaA/(BaA+Chry) and Anth/(Anth+Phen) vs. Fluo/(Fluo+Pyre). Figure 2: Time series concentrations of PAHs in PM\({}_{2.5}\) at the sampling site.
# Pose Tracking and Object Reconstruction Based on Occlusion Relationships in Complex Environments
## 2 Methods
### Key-Frame Pool Strategy Based on Dual-Layer Mask  
In object pose tracking, most existing methods address environmental interference by applying masks to exclude all image content outside the target object, treating the mask's boundary as the object's edge. However, these methods often overlook foreground occlusions on the target object, potentially leading to biased pose estimations. To overcome this limitation, we propose a dual-layer masking approach. The first layer is generated by a segmentation network to isolate the background, while the second layer is computed by using depth information to mark foreground occlusion. This dual-layer mask effectively accounts for occlusions and provides a more accurate representation of the object. Once generated, the dual-layer mask is used to update the key-frame pool, further improving the accuracy of pose estimation.
# Improved Adaptive Federated Kalman Filtering for INS/GNSS/VNS Integrated Navigation Algorithm
## 2 Multi-Source Fusion Model  
Figure 1 shows the structure of the INS/GNSS/VNS integrated navigation system [29; 30]. The system uses INS as the main filter and reference system for the sub-filters, which utilize observation information from the external observation system GNSS and the autonomous observation system VNS to construct sub-filter 1 and sub-filter 2. The INS estimates the position, velocity, and attitude, and the sub-filter observation information is used to correct the system state to obtain a local optimal estimate. The global optimal estimate of the system state is then obtained through global fusion by the main filter.
# Log-Based Fault Localization with Unsupervised Log Segmentation
## 2 Materials and Methods
### Dataset  
We performed our study on real industrial logs from Nokia, which deliver wireless connectivity solutions to many different enterprises. The reliability of communication is expected nowadays, putting pressure on the software and hardware components of the Base Transceiver Station (BTS). The software component of the BTS consists of many components that communicate over the interfaces. Failure of any component often leads to an unacceptable drop in performance. Detecting and fixing faults often can only be performed by comparison with previous normal behaviors.  
However, simple manual comparisons of log files consisting of hundreds of thousands of log lines are not feasible. The dataset used in this study, which is publicly available along the code [29], comprises anonymized logs from Nokia containing three faults. Anonymization was performed by removing the content of the log templates, leaving only template IDs. Thread names and level info details were also anonymized by substituting with the \"thread<num>\" and \"level<num>\" strings. For the first two fault scenarios, we collected 10 logs from normal execution prior to the fault date. For the third one, there was only one normal log and one failed log. The logs were normalized by removing timestamps and lines with interface object descriptions, segmented by thread ID, and processed using Drain. VotingExperts was then used to segment the log template sequences from each thread.
# A Real-Time Map Restoration Algorithm Based on ORB-SLAM3
## 2 Materials and Methods
### Overview of Real-Time Map Restoration Algorithms  
This paper proposes a real-time map restoration algorithm based on ORB-SLAM3. The framework of the system proposed in this paper is shown in Figure 1. In the monocular inertial mode, each image frame will extract ORB feature points and calculate descriptors in the tracking thread. We insert frames that have extracted feature points into a custom stack for reuse. This stack stores all image frames up to the successful initialization of the current active map. In the local mapping thread, when the IMU initialization is successful and the second bundle adjustment (BA) optimization [28] is completed, the frame in the stack is quickly tracked, that is, from the first keyframe of the map to the earlier frame quickly matching and tracking one by one. Moreover, the process is parallel to the tracking thread. The detailed process is demonstrated in Section 2.2. In addition, when the keyframe is inserted from the local mapping thread into the loop closing thread, the grayscale image of the keyframe is adaptively clipped and the grayscale histogram is calculated. After normalization of the grayscale histogram, the standard deviation and mean value are calculated (details refer to Section 2.3). This facilitates the dynamic elimination of common-view frames, detection of candidate frames in loop closing threads, and similarity comparison of grayscale histograms.
# Credible Navigation Algorithm for GNSS Attack Detection Using Auxiliary Sensor System
## 5 Experimental Analysis
### Positioning Accuracy Deterioration Factor  
In order to evaluate the credible navigation effectiveness of ACNA, EKF and RIO, we define the positioning accuracy deterioration factor \(\gamma\) and credibility of the algorithm \(\varsigma\). Here, \(\gamma\) represents the ratio of the \(1\sigma\) positioning accuracy calculated by the algorithm before and after GNSS attacks. The larger \(\gamma\) becomes, the worse the positioning accuracy of the algorithm in terms of resisting the GNSS attacks, and vice versa. The positioning accuracy deterioration factor \(\gamma\) is  
\[\gamma=\frac{\widetilde{\sigma}}{\sigma} \tag{34}\]  
where \(\sigma\) is the \(1\sigma\) positioning accuracy obtained by the algorithm before the GNSS attacks, and \(\widetilde{\sigma}\) is the \(1\sigma\) positioning accuracy calculated by the algorithm after the GNSS attacks.  
The credibility of the algorithm \(\varsigma\) stands for how well each algorithm can help improve on the positioning accuracy deterioration factor \(\gamma\). The smaller the \(\varsigma\), the lower the credibility of the algorithm, and vice versa. The credibility of the algorithm \(\varsigma\) is  
\[\varsigma=\frac{\gamma_{\text{g}}-\gamma}{\gamma_{\text{g}}} \tag{35}\]  
where \(\gamma_{g}\) is the GNSS positioning accuracy deterioration factor after GNSS attacks.  
Table 6 concludes the \(\sigma\), \(\gamma\) and \(\varsigma\) for the three algorithms on data set \(\widetilde{\Psi}_{4}\) before and after GNSS attacks. It can be seen that before any GNSS attack, the three algorithms can achieve a positioning accuracy at decimeter level.  
However, after GNSS attacks, the positioning accuracy deterioration factor of the GNSS itself reaches \(11.80\). Consequently, the positioning accuracy deterioration factor of EKF is \(10.65\), and the credibility of it drops to \(-0.74\%\). The positioning accuracy deterioration factor of RIO is \(6.75\), and the credibility of it is \(36.19\%\). Nevertheless, the positioning accuracy deterioration factor of ACNA is only \(0.81\) because the ACNA algorithm can effectively make the credibility of the algorithm reach as high as \(92.53\%\). Therefore, ACNA can reduce the positioning accuracy deterioration factor better and improve the credibility of the algorithm better when a GNSS attack occurs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & \(\sigma\) & \(\widetilde{\sigma}\) & \(\gamma\) & \(\xi\) \\ \hline GNSS & \(0.59\) & \(11.80\) & \(20.01\) & / \\ EKF & \(0.53\) & \(10.65\) & \(20.16\) & \(-0.74\%\) \\ RIO & \(0.53\) & \(6.75\) & \(12.77\) & \(36.19\%\) \\ ACNA & \(0.54\) & \(0.81\) & \(1.49\) & \(92.53\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: \(\sigma\), \(\gamma\) and \(\varsigma\) for the three algorithms.
# Supervised Dynamic Correlated Topic Model for Classifying Categorical Time Series
## 5 Discussion  
In this paper, we introduced the sDCTM model framework, which aims to classify categorical time series by dynamically modeling the underlying latent topics that capture the hidden thematic structure of the time series collection. We demonstrated the applicability of our method to real data by applying it to the promoter identification data to classify _E. coli_ DNA sub-sequences based on promoter presence. Using sDCTM, we estimated the dynamic latent topic structure that serves as markers for promoter presence in a DNA sub-sequence and classify the sub-sequences. Among the \(J=3\) latent topics obtained using sDCTM, we observed that nucleotide levels \(a\), \(c\), and \(g\) were highly probable within Topic 1, nucleotide level \(t\) was highly probable within Topic 2, and nucleotide level \(a\) was highly probable within Topic 3. In addition, the logoplot based on the model predictions showed higher conservation at various positions in DNA sub-sequences with predicted promoter presence in comparison with DNA sub-sequences with predicted promoter absence. We compared our method with the k-NN, SVM, and classification tree method on a train-test data setup. Our comparative study results indicated that the sDCTM model performed better than the other classification techniques in both training and test datasets. This indicates that the estimated underlying latent topic structure captured by the sDCTM model is able to identify the promoter presence/absence in a DNA sub-sequence better in comparison with the other classification approaches. An extension of the sDCTM model accommodating word time series of varying lengths can be derived as a part of future work.  
**Author Contributions:** Conceptualization, N.P., N.R. and S.R.; methodology, N.P., N.R. and S.R.; formal analysis, N.P., N.R. and S.R.; investigation, N.P., N.R. and S.R.; data curation, N.P.; writing--original draft preparation, N.P., N.R. and S.R.; writing--review and editing, N.P., N.R. and S.R.; visualization, N.P., N.R. and S.R.; supervision, N.R. and S.R.; project administration, N.R. and S.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Train Accuracy** & **Test Accuracy** \\ \hline sDCTM & 100\% & 90\% \\ k-NN & 84.8\% & 85\% \\ Classification tree & 87.2\% & 85\% \\ SVM & 100\% & 55\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparing accuracy using the sDCTM, k-NN, classification tree, and SVM methods on the train and test data.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data and analysis code are available at [[https://github.com/Namitha/VionaPais/sDTCM](https://github.com/Namitha/VionaPais/sDTCM)]([https://github.com/Namitha/VionaPais/sDTCM](https://github.com/Namitha/VionaPais/sDTCM)) (accessed on 10 June 2024), Github link.  
**Conflicts of Interest:** The authors declare no conflicts of interest.