esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 High-Performance LU Decomposition
### Partial Refactorisation Concept
First, the column index \(k=\min(\hat{i},\hat{j})\) of the diagonal entry \((k,k)\) is determined among the entries in the \(\mathbf{LU}\) matrix and is added to the factorisation path. Then, beginning from the diagonal entry \((k,k)\), the main loop of the algorithm starts. In each iteration, the algorithm finds the closest off-diagonal entry in the \(\mathbf{LU}\) matrix. If the closest entry \((\hat{i},\hat{j})\) is above the diagonal, it chooses \(k=\hat{j}\), and if the entry is below, it chooses \(k=\hat{i}\). Finally, the  
Figure 3: \(\mathbf{L}\) matrix of a symmetric LU decomposition, with changing entries in \((2,2)\) and \((4,4)\). The singleton path is marked in red for \((2,2)\) and in green for \((4,4)\). column index \(k\) of the corresponding diagonal entry \((k,k)\) is appended to the factorisation path. The loop finishes if either no further off-diagonal entry is found or \(k=n\), where \(n\) is the number of rows/columns of the matrix. The algorithm above considers a single changed value, and it delivers a so-called _singleton path_. The union of all singleton paths, starting from \((1,1)\), is the full factorisation path of a matrix. The full factorisation path contains all columns of the **LU** matrix and will not achieve any performance improvement if used during refactorisation, since the iteration over the full factorisation path will result in the recomputation of all columns of the **LU** matrix. The benefit of this full path is that it encodes the inner column dependencies of the **LU** matrix; that is, it encodes which columns are dependent on one another and, thus, if any column changes numerically, which columns will be affected and need to be recomputed. It can be used to determine the columns to be refactorised if only a subset of values vary in the original matrix. This potentially causes a performance improvement, since the corresponding factorisation path no longer includes all of the columns. The described algorithm has to be performed on the **LU** matrix, since this matrix contains, in addition to ordering permutations, the partial pivoting permutation and the fill-in values, which affect the inner column dependencies that the factorisation path algorithm determines. Therefore, the algorithm can only be performed after a first LU factorisation has been performed. The first full LU factorisation can be performed in the simulator's initialisation phase (see Figure 2), and then the computationally less expensive partial refactorisation is executed during the simulation phase.
## 6 Conclusions  
This work presents a proposed simulation of an IS (Importance Sampling) technique to speed up the Monte Carlo simulation for the MMPP/D/1 queue. In traditional Importance Sampling (IS), the arrival rate is augmented in order to generate a significant number of rare occurrences. Our idea does not include increasing the arrival rate, but rather decreasing the service rate. Using this method, we may calculate the chance of the queue length exceeding a certain value in an MMPP/D/1 queue.  
The authors in [6] also introduced a method to estimate the tail probability of FIFO queue length in real time. Nevertheless, the authors employed a Poisson process for the arrival process, but in this research, we utilized an MMPP for the arrival process. The determination of the service rate in the MMPP arrival process is more intricate compared with a Poison procedure in a FIFO queue.  
Our technique provides the benefit of not altering user traffic, making it suitable for use in the actual network. The results of the aforementioned simulation demonstrate that our suggested technique achieves accuracy that is comparable to traditional IS and MC methods. However, our proposed method's simulation time is approximately 3200 times faster than that of MC.  
In addition, in conventional IS, four parameters need to be changed: the arrival rates \(\lambda_{0}^{*},\lambda_{1}^{*}\) and the state transition probabilities \(r_{01}^{*},r_{10}^{*}\). However, we only change the service rate; therefore, our proposed method can be performed more easily than conventional IS.  
We also investigated the effect of the state information in conventional IS simulation. We show that the formula for the weighting function (18) does not depend on states \(i\) and \(j\). In this way, we can perform an IS simulation for the two-state MMPP/D/1 queue. Otherwise, if we remove the state information from Equation (18), then the simulation time will be slightly reduced.  
In our future work, we will study n-state MMPP/D/1 in cases of finite buffer models and more complicated models. In addition, we will study some effective IS algorithms, such as Adaptive Importance Sampling (AIS) for queues.  
Conceptualization, N.N.H.; methodology, N.N.H. and P.T.G.; writing, N.N.H.; review and editing, N.H.A. and P.T.G.; funding acquisition, N.H.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Vietnam Academy of Science and Technology under Grant No. VAST01.09/22-23.  
Not applicable.  
Data Availability StatementData is contained within the article.
# Radio Frequency Interference, Its Mitigation and Its Implications for the Civil Aviation Industry
## 2 Theoretical Background
### Challenges in Mitigating RFI
#### 2.4.9 UAV-Based RF Monitoring Platforms  
UAVs equipped with RF sensors provide a mobile platform for interference detection [14]. These systems are especially useful for monitoring interference in areas where traditional ground-based equipment may not be effective, such as airport perimeters or remote locations. The use of these tools ensures that aviation systems remain resilient to interference, enhancing safety and operational reliability. By enabling the precise detection, comprehensive analysis, and validation of mitigation strategies, this equipment plays a pivotal role in protecting critical aviation infrastructure from the risks posed by RFI.
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
## 5 SA-Based 5G V2X Services
### Network Support
#### 5.2.1 Core Network  
5G System Support  
The 5G system plays an important role in specifying the V2X communication and configuration. The technical specification document (_TS 24.587 Rel-16_) discusses the protocol aspects for V2X services (in terms of messages transport) in 5 GS and interworking to EPS considering the architectural enhancements of technical specification (_TS 23.287_). The transmission of V2X messages occurs over PC5 and/or Uu interfaces. In this connection, two protocols for data transmission and V2X communication are specified: among the UEs over the PC5 interface; between the UE and V2X application server over the Uu interface. Apart from the procedures described in the specification, it includes message functional definition and contents; information elements coding; coding other than information element coding; and a list of system parameters. In _Rel-17_, the updated changes involve security context identity for PC5 unicast, mutual authentication for PC5 unicast link, PC5 unicast link establishment for broadcast, alignments for providing an indication of activation of the PC5 unicast signaling security to lower layer, etc.  
The 5G-enabled V2X communication messages are transmitted and received to and from UEs following certain important and actionable policies as described in the technical specification (_TS 24.588 Rel-16_). For the UE policies configurations in 5 GS, the stated specification considers the architectural enhancements support (discussed in _TS 23.287 Rel-16_) and the protocol aspects in 5 GS (discussed in _TS 24.587 Rel-16 and Rel-17_). Furthermore, the latter documents also define the UE policy procedures for interfaces i.e., V2X communication over PC5 and Uu. For V2X communication over the given two interfaces, the encoding of UE policies is specified in the document (_TS 24.588_). Apart from the given specification, the UE policy delivery service is specified in (_TS 24.501_), which helps in providing the UE policies to the UE e.g., UE PC5 unicast signaling security policy, UE PC5 unicast user plane security policy, etc. The research community focusing on proposing the UE policies in 5 GS is encouraged to follow the given descriptions and encoding schemes for V2X communication over PC5 and Uu interfaces.
## 5 Sensitivity Analysis of Parameters
### Tunnel-to-Dewatering Well Distance (d)  
Five tunnel-to-dewatering well distances were used for the sensitivity analysis, which were 10 m, 15 m, 20 m, 25 m and 30 m. The resulting tunnel settlement and bending moment curves, obtained through the methodology presented in this work, are depicted in Figures 10 and 11. From the observations, it is evident that an increase in the spacing leads to a reduction in both the vertical settlement and the bending moment experienced by the tunnel. Specifically, as d increases from 10 m to 30 m, the maximum vertical settlement of the tunnel decreases from 6.37 mm to 4.28 mm, and the maximum bending moment value decreases from 4.63 MN\(\cdot\)m to 1.44 MN\(\cdot\)m. This reduction indicates a substantial influence. The reason behind this behavior is that the water level experiences fewer fluctuations as one moves further from the dewatering well. Consequently, there is reduced stress in the proximity of the tunnel, resulting in a diminished impact on the tunnel as it is positioned farther from the dewatering well.  
Figure 10: Curves of tunnel displacement under different spacing \(d\).  
Figure 9: Curves of the tunnel’s bending moments under different permeability coefficients of soil \(k_{\text{t}}\).
# Wind Lidar and Radiosonde Measurements of Low-Level Jets in Coastal Areas of the German Bight
## 1 Introduction
The article presents two LLJ statistics obtained with a wind lidar, with a duration of one year each, one data set at the island Heligoland in the German Bight (24 March 2017 to 23 March 2018), and one at the East Frisian island Norderney, Germany (9 August 2019 to 8 August 2020). The statistics for both time periods are complemented by radiosonde observations at the site Norderney performed twice per day, which provide information on stability as well (cf. also Table 1). The LLJ occurrence and typical features for the two one-year data sets at different sites are discussed. Further, a set of four wind lidars have been operated simultaneously (23 May 2017 to 19 June 2017). To study the spatial extent of LLJ events, two cases are analysed with different lidar systems distributed throughout the German Bight. One case study of an LLJ observed during the daytime and under stable conditions is 2 June 2017, and one LLJ observed during the night is later on the same day. The LLJ events were observed by lidar measurements at the above-mentioned sites, the daytime event additionally by airborne observations [52; 53] obtained during the project WIPAFF [4] to investigate wakes. The case was further simulated with the mesoscale weather research and forecast (WRF) model. The spatial extent of the observed LLJ is discussed. In Section 2, the methods used in this study are introduced. In Section 3, the study sites and the observational wind data sets are presented. In Section 4, the numerical simulation is shown. In Section 5, different LLJ statistics are analysed and discussed. Further, the spatial extent of the LLJ is estimated. Finally, Section 6 provides conclusions about the \begin{table}
\begin{tabular}{l c c c} \hline \hline
**ID (Location)** & **Latitude** & **Longitude** & **Data Period** \\ \hline HL (Heligoland) & 54.1773\({}^{\circ}\) N & 7.8919\({}^{\circ}\) E & 24 March 2017–23 March 2018 \\ \hline NO (Norderney) & 53.7125\({}^{\circ}\) N & 7.1522\({}^{\circ}\) E & 9 August 2019–8 August 2020 \\ \hline FI (FINO1) & 54.0149\({}^{\circ}\) N & 6.5876\({}^{\circ}\) E & 1 April 2017–31 March 2018 \\ \hline AV (Alpha-Ventus) & 54.0000\({}^{\circ}\) N & 6.6233\({}^{\circ}\) E & 1 April 2017–12 December 2017 \\ \hline SPO (St. Peter-Ording) & 54.3281\({}^{\circ}\) N & 8.6015\({}^{\circ}\) E & 23 May 2017–19 June 2017 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Location coordinates (WGS 84) and data period of the wind lidar systems used in this study. impact of wind profiles, LLJ importance, and the wind field modifications by coastal effects for offshore wind energy.
# Steady-State Response Analysis of an Uncertain Rotor Based on Chebyshev Orthogonal Polynomials
## 4 Numerical Results
### Effect of Interval Mass Unbalance  
Unbalance in a rotor is unavoidable, due to manufacturing and assembly errors. The mass unbalance is considered as an interval variable, one expressed in the following form:  
\[[\underline{e},\overline{e}]=[e-\beta_{e}e,e+\beta_{e}e] \tag{16}\]  
where \(e\) is the nominal value (30 g-cm) of mass unbalance in Discs 1, 2, and 3, and \(\beta_{e}\) is the degree of uncertainty. The steady-state response of the rotor system is calculated for uncertainty degrees of 5%, 10%, and 15%, respectively. Similar to the convergence analysis described in Section 4.1, a third-order (\(p=3\)) Chebyshev orthogonal polynomial with four  
Figure 6: Convergence analysis of \(\varepsilon_{r}\) under varying degrees of uncertainty in support stiffness. (**a**) 5% (**b**) 10% (**c**) 15%.  
Figure 7: Dynamic response range of Disc 1, with intervals of support stiffness.  
Gaussian integration nodes in each dimension has been constructed. The variation in the steady-state response of Disc 1 is shown in Figure 8.  
Figure 8 illustrates that the range of steady-state amplitude widers as the rotational speed increases, indicating that higher rotational speeds amplify the effect of mass unbalance uncertainty on the vibration amplitude of the rotor system. Additionally, the upper and lower bounds are symmetric about the deterministic curve, while the critical speed remains unchanged.
## 5 Discussion
### Detection Channel Transmission Quality  
According to the judgment criteria of channel quality in Section 4.2, the following conclusions could be found through further research. If the mean value \(\mu\) of two channel matrices \(X\) is the same, the corresponding \(t\) value can be calculated. According to the characteristics of the normal distribution curve, the smaller the variance \(\sigma^{2}\) is, the larger the corresponding \(t\) value will be. When the mean value \(\mu\) is constant, the greater the value of \(t\), the smaller the difference between the singular values of the channel matrix. The more uniform the singular value distribution, the better the corresponding channel transmission quality.  
If the variance \(\sigma^{2}\) of two channel matrices \(X\) is the same, the normal distribution probability density curve is the same shape, except that the position of the axis of symmetry is different. According to the characteristics of the normal distribution curve, the axis of symmetry of the curve is far from the coordinate origin. That is, the greater the mean value \(\mu\) is, the greater the integral \(t\) value of the curve on the positive half axis of axis \(x\). Therefore, the corresponding channel transmission quality is better.  
Therefore, according to the \(t\) value of each channel matrix, the quality of channel transmission can be measured. Then, the upstream channels between all user nodes and relay nodes can be sorted according to the transmission quality. Determine the order of all channels from the worst to the best channel transmission quality. The two interactive channels with the worst transmission quality are selected first, and the transmission precoding vector and alignment direction are calculated.
## 4 Discussion
### Probiotics in Halitosis  
Halitosis has multiple etiologies, and may be caused by ingestion of certain foods, poor oral hygiene, periodontitis, respiratory infections, tobacco consumption, genetic predisposition, dry mouth and oral microbiome dysbiosis [102]. Volatile sulfur compounds (VCS) responsible for halitosis include hydrogen sulfide, methyl mercaptan and dimethyl sulfide. In the oral cavity, these substances essentially result from the metabolic activity of oral microorganisms [102]. Several factors contribute to the production of these compounds, such as higher prevalence of gram-negative anaerobic bacteria, alkaline salivary pH, low redox potential, and the presence of sulfuric substrates (cysteine and methionine) [103; 104]. Four studies have looked at the impact of probiotics in reducing halitosis by decreasing the density of the bacteria responsible by VCS production (Table S9).  
[PERSON] et al. [105] assessed the effect of consumption of _Weissella cibaria_ tablets for 8 weeks on halitosis in healthy adults. _W. cibaria_ counts were higher in the probiotic group at 4 and 8 weeks, and there was a significant reduction in VCS levels at week 4, and a significant reduction in bad breath improvement (BBI) score at week 8. The results suggest that _W. cibaria_ tablets can be a useful oral hygiene product to control bad breath.  
_Streptococcus salivarius_ M18 has also been shown to reduce halitosis in patients with orthodontic treatment [106]. This required a month of consumption of two probiotic lozenges per day. Only _Rothia_ spp. levels were significantly reduced in the probiotic group. The VCS score decreased significantly throughout the study in the probiotic group and placebo groups after 1 month but, after 3 months of follow-up, the VCS levels returned to the baseline value in the placebo group whereas in the probiotic group the VCS levels decreased significantly. A reduction in halitosis levels was also observed 1 and 3 months after ingestion of _L. salivarius_ and _L. reuteri_ for 14 days in patients with chronic periodontitis and halitosis [46]. In a similar study performed with _L. salivarius_ WB21 only, VCS levels were significantly reduced and there was a significant reduction of PD [107]. Also, bacteria known to produce malodourous compounds like _F. nucleatum_ were reduced in the probioticgroup. Finally, in [PERSON] et al. [108]_L. reuteri_ reduced the organoleptic scores in patients who had a subjective feeling of bad breath.  
In summary, regular consumption of probiotics _L. salivarius, S. salivarius, W. cibaria_ or _L. reuteri_ may complement mechanical oral care in controlling halitosis.
## 2 Materials and Methods
### Micro-CT Examination and Image Analysis  
Micro-CT scan was performed using a Skyscanner 1172 (Bruker Kontich, Belgium) with kV X-ray source, 100 mA (pixel size 16.89 \(\upmu\)m), and an aluminium + copper filter. Data were then reconstructed with NRecon software (Version: 1.7.1.6) and a circular region of interest 10 mm in diameter was applied to all micro-CT slices using CTAn software (Version: 1.17.7.2, Bruker) to include the original 8 mm defect region and an additional 2 mm of bone layer surrounding the scaffold and defect.  
Each of the 23 scaffold samples contained between 500 and 700 individual micro-CT slices. To accommodate for limitations in processing large amounts of data, all slices for each scaffold sample were divided into three equal sub-sets, where Sub-set 1 was always closest to the knee joint and Sub-set 3 was always the furthest from the knee joint  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & **Scaffold** & **Group Size** \\ \hline
1 & HA–collagen & 6 \\
2 & HA–collagen + BMC & 6 \\
3 & Ti–PLA–collagen/PLGA & 5 \\
4 & Ti–PLA–collagen/PLGA + & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Scaffold group, composition, and sample size. One sample in Group 3 was lost during data acquisition.  
Figure 1: Composition of the tested scaffolds. (**a**) Collagen/hydroxyapatite scaffold; and (**b**) Ti–PLA–collagen/PLGA scaffold.  
(Figure 2). All micro-CT slices per sub-set were first imported into ImageJ. These were then stacked into a single file and converted to 8-bit binary images (Figure 3a) using threshold adjustments; control samples could be converted automatically whilst Ti-PLA-collagen/PLGA samples required manual conversion to ensure that ImageJ did not mistake the titanium for bone tissue. BoneJ, an ImageJ plugin developed specifically for quantified data extraction from microscopic and macroscopic skeletal samples [31; 32], was then used to collect TbTh, BV/TV, and DA data.  
a BV/TV ratio in the form of a percentage is provided. Finally, the degree of anisotropy (DA) was assessed to provide insight into the orientation and biomechanical durability of newly deposited tissue [32; 34]. Standard values for directions (2000), lines per direction (10,000), and sampling increment (1.73) which are built within Bone] were used to generate the DA. The value provided is always between 0 and 1, where larger values indicate more homogenous bone deposition and thus increased biomechanical stability [34; 35].
## 4 Result Analysis  
### Comparison of Annotation Efficiency  
As demonstrated in Table 7, a detailed analysis of the experimental data confirms the substantial advantages of the vertical domain model dynamic dataset construction method proposed in this study. By employing multi-role collaboration and intelligent annotation, the method achieves superior performance in annotation efficiency, accuracy, and consistency. Regarding dynamic classification adjustment response time, the proposed method demonstrates exceptional performance, reducing the adjustment process to the minute level. When new classification requirements or changes in data distribution occur, traditional methods necessitate redeveloping annotation strategies and retraining annotators, which is highly time-consuming. In contrast, the method presented in this study rapidly adapts to dynamic classification adjustments through close multi-role collaboration and prompt updates to intelligent annotation algorithms. Data augmentation personnel swiftly modify augmentation strategies to accommodate new classifications, automatic annotation per  
Figure 6: Data augmentation files and prompt boxes for river and lake water pollution data. sonnel apply the updated model to generate accurate annotations, and reviewers perform timely evaluations to ensure data quality. In the area of data augmentation, this method also shows outstanding performance. Traditional data augmentation typically requires manual annotation before augmenting data in the program, which increases the annotators' workload and hampers reviewers' ability to visually inspect results. In contrast, this method leverages image transformation and data synthesis technologies to quickly generate large volumes of augmented data. Automatic annotation personnel then use the developed tools to annotate the augmented data efficiently. Timely review and feedback from reviewers further guarantee annotation accuracy and consistency, minimizing repetitive labor stemming from mis-annotations. In terms of consistency, the proposed method significantly enhances annotation consistency through an integrated consistency-checking mechanism. By comparing annotations from different annotators, this mechanism can promptly identify and rectify inconsistencies, ensuring high levels of accuracy and coherence. In comparison, traditional annotation approaches lack effective consistency checks, making it difficult to achieve reliable and uniform results. Comparative experiments with LabelImg, LabelMe, and Make Sense highlight the clear advantages of this method across annotation efficiency, accuracy, and consistency metrics. Notably, when classification modifications occur, the number of images processed
## 3 Approaches for Quantifying the Precision, Stability, Accuracy, and Uncertainty of COSMIC-2 Data
### Stability Estimate
Figure 7 demonstrates the stability of COSMIC-2 data with those RO missions over the lower troposphere and in the lower stratosphere below 40 km. Figure 7: Monthly plots for fractional refractivity differences between COSMIC-2 and other RO missions (**a**) from the surface to 5 km altitude, (**b**) from 5 km to 15 km altitude, (**c**) from 15 km to 25 km altitude, (**d**) from 25 km to 40 km altitude, and (**e**) from 40 km to 60 km altitude from October 2019 to January 2020.