esa-sceva/satcom-chunk-collection huggingface dataset:
# Characteristics of 2.45 GHz Surface-Wave-Sustained Argon Discharge for Bio-Medical Applications
## 3 Results
### Effects of the Dielectric Tube Permittivity and the Wave Frequency  
The dielectric permittivity is one of the important characteristics of the discharge tube, which is a part of the waveguide structure for the wave propagation. Using different materials for the discharge tube, one can obtain higher or lower plasma density and temperature at the same geometry parameters and wave power. Figure 4 illustrates the changes in the plasma density (electron number density) \(n_{\text{e}}\) and the wave power axial distributions at two different dielectric permittivity values (\(\epsilon_{\text{d}}\) = 4 and 5.58) of the same size discharge tube (8 mm/2 mm, see Table 1). For easily comparing the results, the end of the plasma torch is at position \(z=0\), and the surfatron position is on the left side of the graph. The well-known almost linear axial distribution of the plasma density is confirmed for all discharge conditions. The effect of the dielectric permittivity increasing is in the increasing in the plasma density, but it is not very significant.  
Figure 3: Schematic of self-consistent modeling approach.  
The increase in the wave frequency turns out to produce a much more visible result in decreasing the plasma density combined with increasing the wave power necessary for its sustaining. This is seen in comparison with the black and green curves in Figure 4.
## 2 Related Work  
Many research papers on RCA focus on complex large-scale systems [1; 2; 3; 4]; they can be grouped into the following categories:  
**Event correlation analysis-based methods**. These methods correlate activity events to identify the root cause of an anomaly or failure. [PERSON] et al. [5] introduced a new model of statistical inference to manage complex IT infrastructures based on their anomaly events data obtained from an intelligent monitoring engine. [PERSON] et al. [6] proposed a parametric model to describe noisy time lags from fluctuating events.  
**Log-based methods**. The common approach to RCA is to analyze log files to identify problems that occurred in the system. The problems are then examined to identify potential causes. The authors in [7; 8] introduced a classic log mining method to diagnose and locate anomalies in traditional distributed systems. According to the research in [9; 10; 11], the log mining method also plays an important role in the RCA of cloud applications. However, not all abnormal behaviors are recorded in logs:in many cases of RCA, in addition to log mining, O&M personnel have to combine their domain experience to find the root cause.  
**Execution path mining-based methods**. The execution path mining-based method is usually suitable for online RCA. It often traces transaction flows between services or components and provides a clear view to identify problem areas and potential bottlenecks. There is already much research on this and many popular tools, such as ptrace [12], Pinpoint [13], AutoPath [14], and CloRExPa [15]. This path mining kind of method can usually solve some intuitive problems, but, for problems requiring inferring causality, the execution path mining-based method usually does not work well.  
**Dependency graph mining-based methods**. In recent years, more and more studies have been based on dependency graphs. In the RCA research for cloud applications, the most representative are related studies from [PERSON] et al. [16; 17; 18; 19], [PERSON] et al. [20], [PERSON] et al. [21], and [PERSON] et al. [22]. These methods are usually used to find the root cause of the problem at the component and service levels to establish a dependency or causality graph and a cause and effect graph based on performance indicators. Most of those authors claimed that the methods they proposed can completely solve the problem of RCA without expert knowledge.  
We have found no relevant work on combining a knowledge graph and a causal search algorithm to find the root cause of a defect or failure in a cloud platform.  
Inspired by the paper from [PERSON] et al. [23], it is not difficult to draw a conclusion that automated root cause analysis reduces the overall dependency on expert knowledge, but it doesn't diminish the value of on-site experts who are vital in monitoring, validating, and managing the RCA process. Therefore, the proposed method is based on a dependency graph-based method, but we introduce knowledge graph technology, which is a kind of knowledge database. As a result, the proposed method is more scalable.
# Integrity Monitoring of GNSS Constellations with Only LEO-PNT Satellites +
###### Abstract  
This paper explores the usage of LEO-PNT (Positioning, Navigation, and Timing) for providing navigation integrity to GNSS (Global Navigation Satellite System) constellations. LEO mega-constellations, which are positioned between GNSSs and users, offer closer-to-the user geometry, improving performance, reducing the time to alarm (TTA) and enabling integrity monitoring without complex ground segments of any sort. The aim is to use future LEO mega-constellations as integrity monitors for a forthcoming European Global Navigation Satellite System (EGNSS) specifically focused on automotive users, which has minimal onboard satellite capabilities and no ground involvement. This plan builds on earlier studies, anticipating the performance of the upcoming LEO-PNT In-Orbit Demonstration.  
LEO-PNT; integrity; TTA +
Footnote †: This paper explores the usage of LEO-PNT (Positioning, Navigation, and Timing) for providing navigation integrity to GNSS (Global Navigation Satellite System) constellations. LEO mega-constellations, which are positioned between GNSSs and users, offer closer-to-the user geometry, improving performance, reducing the time to alarm (TTA) and enabling integrity monitoring without complex ground segments of any sort. The aim is to use future LEO mega-constellations as integrity monitors for a forthcoming European Global Navigation Satellite System (EGNSS) specifically focused on automotive users, which has minimal onboard satellite capabilities and no ground involvement. This plan builds on earlier studies, anticipating the performance of the upcoming LEO-PNT In-Orbit Demonstration.  
EPEP-PNT; integrity; TTA +
Footnote †: This paper explores the usage of LEO-PNT (Positioning, Navigation, and Timing) for providing navigation integrity to GNSS (Global Navigation Satellite System) constellations. LEO mega-constellations, which are positioned between GNSSs and users, offer closer-to-the user geometry, improving performance, reducing the time to alarm (TTA) and enabling integrity monitoring without complex ground segments of any sort. The aim is to use future LEO mega-constellations as integrity monitors for a forthcoming European Global Navigation Satellite System (EGNSS) specifically focused on automotive users, which has minimal onboard satellite capabilities and no ground involvement. This plan builds on earlier studies, anticipating the performance of the upcoming LEO-PNT In-Orbit Demonstration.
# No Free Lunch--Characterizing the Performance of 6 TiSCH When Using Different Physical Layers
## 2 Related Work
### Performance Improvement and Evaluation of 6 TiSCH  
[PERSON] et al. [21] evaluate the performance of the full 6 TiSCH stack on the OpenWSN reference architecture in terms of responsiveness to time critical event triggers. They vary the number of active slots in a frame of length 11 slots and measure how packet end-to-end latency is affected.  
[PERSON] et al. [22] evaluate the performance of the 6 TiSCH stack with the deployment of traffic isolation mechanisms that allow reservation of dedicated slots for certain applications and reservation of shared slots for alarm events. They report end-to-end latency and Packet Delivery Ratio (PDR) under various schedule management strategies, including using uniformly distributed shared cells instead of contiguous (adjacent) shared cells in the slot-frame.  
[PERSON] et al. [23] execute a performance evaluation of the 6 TiSCH protocol stack in an indoor environment with a focus on the stability of the stack performance. They report the end-to-end reliability and the number of parent changes as the network is converging. They also propose a stable link quality metric and a simplified method for schedule inconsistency management.  
[PERSON] et al. [24] evaluate the performance of the 6 TiSCH stack in co-located networks. They consider both cases where the coexisting 6 TiSCH networks are either synchronized or un-synchronized.  
In all of these related works, the PHY used is IEEE802.15.4 O-QPSK 2.4 GHz. [PERSON] et al. [5] are the exception, as they provide an experimental evaluation of IEEE802.15.4e TSCH Medium Access Control (MAC) based on IEEE802.15.4g FSK 868 MHz radio. The experiments report range and performance testing results in terms of PDR and Packet Error Rate in four situations: Line-of-Sight conditions, Non-line-of-sight conditions, tree topology in corridor setting and line topology across buildings.
# A Comprehensive Survey on Knowledge-Defined Networking
## 7 Application Plane
### Application Scenarios
#### 7.3.6 Cloud Computing  
Cloud computing refers to the delivery of on-demand computing resources, including servers, storage, applications, and services, over the internet. In a cloud computing environment, by designing cloud computing applications with policies for network function virtualization, network functions can be achieved by utilizing cloud computing resources [533]. A framework that integrates cloud computing platforms with network orchestration and compares single controller orchestration with orchestration with application based network operations has been studied in [534]. An optimal resource allocation and virtual machine placement model for multi-tier applications in large cloud data centers that optimizes the data center's energy and communication costs that influence the cloud's performance has been investigated in [535]. In [536], QoS levels for each application are defined, and queuing for different application flows is scheduled at the switches for end-to-end QoS-guaranteed routing for cloud applications and services. A cloud-enabled secure IoT architecture called CENSOR, which uses a scalable software remote attestation scheme that ensures integrity to achieve application-specific goals in the network, has shown improved data communication and reduced overhead [537]. Cloud storage can be secured by using cloud computing applications that have policies integrating blockchain to create a deep trust between the transaction levels and the controller in KDN, which provides reliability and versatility [538]. Cloud computing can be useful in the event of natural or man-made disasters for providing rapid situational awareness. Driven by such an application, work in [539] proposes an incident-supporting visual cloud computing solution to collect, compute, and consume at the network edge, coupled with cloud offloading to a core computation using knowledge-defined networking.
## 2 Materials  
Figure 2 shows the particle size distribution curves of CG (Figure 2a) and FA (Figure 2b), acquired using high-performance dry dispersion technology (Jinan Winner 3009B, Jinan Winner Particle Instrument Stock Co., Ltd., Jinan, China). CG was obtained by secondary sieving of natural CG from the Xinjulong coal mine, whereas FA was taken from Datang Henan power plant.  
Table 1 shows the chemical compositions of both CG and FA materials, which were measured at Jiangsu Design Institute of Geology for Mineral Resources (China) using the XRF technique. It can be observed that samples of CG and FA, which were dried, ground and sieved (through a 75-\(\mu\)m sieve) prior to analysis, are mainly composed of silicates, aluminates and other minor oxides.  
_Bacillus pasteurii_ (_B. pasteurii_), herein employed as the target bacteria species, was obtained from Shaanxi Microbial Species Resource Conservation Center (China) with the  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Chemical Composition (\%)** & **K\({}_{2}\)O** & **Na\({}_{2}\)O** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **CaO** & **MgO** & **TiO\({}_{2}\)** & **MnO\({}_{2}\)** & **P\({}_{2}\)O\({}_{5}\)** \\ \hline Coal gauge & 1.47 & 0.14 & 80.57 & 6.74 & 2.32 & 2.08 & 0.85 & 0.28 & 0.06 & 0.04 \\ Fly ash & 2.11 & 0.65 & 49.17 & 28.28 & 4.63 & 3.55 & 0.84 & 1.12 & 0.06 & 0.31 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of CG and FA from XRF analysis.  
Figure 2: Particle size distribution of (a) CG and (b) FA.  
strain number A484. In a previous study [21], it was domesticated to be resistant to CG leaching solution and 1 M urea solution simultaneously. The basic culture medium was composed of 5 g/L tryptone, 3 g/L beef extract, a buffer regulator (0.42 g/L NaHCO\({}_{3}\) and 0.53g/L Na\({}_{2}\)CO\({}_{3}\)), deionized water and a certain amount of KOH to obtain a pH of 8.5. All mediums and equipment involved in the basic bacterial culture process were sterilized at 121 \({}^{\circ}\)C for 30 min [35].  
_B. pasteurii_ was firstly moved from the agar slant into the culture medium with an inoculating needle, and then incubated at 30 \({}^{\circ}\)C in a shaker, rotating at 120 rpm for 18 h, to obtain the seed cultivation. Afterwards, the seed cultivation was placed into the basic culture medium at a ratio of 2% \(v/v\), and then in a constant temperature (at 30 \({}^{\circ}\)C) shaker (rotating at 150 rpm), followed by incubation. After 24 h, the strain matured, and the bacterial cultures derived from our custom research procedure were used for subsequent experiments.  
Two mineralizing solutions, made with CaCl\({}_{2}\) and urea, respectively, were used in this study. CaCl\({}_{2}\) was autoclaved, whereas urea was filtrted with a 0.22 \(\upmu\)m microporous filter membrane. The final concentration of both CaCl\({}_{2}\) and urea solutions was 0.5 M [36].
## 2 Numerical Modelling
### Mesh Convergence Analysis  
All of the components of the sandwich plate model (skins and core) use solid elements, with five elements along the thickness direction. A detailed convergence analysis was done on a simplified model which did not include the ballistic pendulum. This is supported by observations that the numerical results are not significantly influenced by the discretisation of the elastic ballistic pendulum components. The boundary conditions (BC) on the sandwich plate are similar to those  
\begin{table}
\begin{tabular}{l l r r} \hline \hline
**Property** & **Units** & **Explosive (C4)** & **Air** \\ \hline \(\rho_{0}\) & kg/m\({}^{3}\) & 1601 & 1.23 \\ \(\bar{E}\) & GJ/m\({}^{3}\) & 7.0 & \(2.58\times 10^{-4}\) \\ \(D\) & m/s & 8193 & – \\ \(R_{\rm CJ}\) & GPa & 28.0 & – \\ \(A\) & GPa & 609.8 & – \\ \(B\) & GPa & 12.95 & – \\ \(R_{1}\) & – & 4.5 & – \\ \(R_{2}\) & – & 1.4 & – \\ \(\omega\) & – & 0.25 & – \\ \(\gamma\) & – & – & 1.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Material properties for the high-explosive (C4) and air used in the MM-ALE numerical models [30; 47].  
imposed by the actual clamping system. However, the overall displacement in the sandwich is slightly overestimated in comparison with the measured experimental displacement. The mesh convergence study can still be achieved despite this difference.  
The average displacement history of the centre of the sandwich panel is shown in Figure 3(a), for various mesh densities between 3375 and 84,375 elements. All of the finite element meshes are structured and regular, with a minimum of 5 integration points along the thickness. The example case shown corresponds to the NL20-10 sandwich plate, and is selected because the average displacement of the sandwich plate shows significant fluctuations during the blast loading. Initially, the displacement increases steeply in all meshes at almost constant slope. The maximum displacement, however, is lower for the coarser meshes. As the element size decreases the simulations clearly converge to a maximum value, with very similar trends. Figure 3(b) shows the final displacement of the rear and front faces of the sandwich plate as a function of the total number of elements.  
It can be clearly observed that the influence of the discretisation on the results vanishes for mesh densities above \(0.1\times 10^{5}\) elements. Additionally, it should be considered that the mesh density has a strong impact on the computational effort and, consequently, a mesh of \(2\times 2\) [mm\({}^{2}\)] (\(45\times 45\times 5\)) on the plane on the plate, corresponding to 37,500 elements, is chosen as the ideal compromise between the quality of the results and computational efficiency.
## 2 Design of WPCS  
### Methodology  
The technical scheme for the novel WPCS was crafted based on the following fundamental principles:  
1. The WPCS was designed as a rotary wireline coring system, similar to the Extended Coring System (ECS) for the drilling vessel JODES Resolution. It features a maximum outer diameter of 96 mm, compatible with 5-inch and 54-inch drill strings to optimize operational efficiency. The pressure chamber within the WPCS sustains pressures up to 35 MPa (5000 psi). Core samples collected by the WPCS measure 2 inches in diameter, with a maximum length of 3.5 m [31; 32].
2. In accordance with the gas hydrate phase equilibrium principles, the WPCS is designed to maintain in situ pressure upon retrieval from the formation. Post-recovery on deck, the pressure chamber is detached from the WPCS via a quick link system. This process controls temperature rise in the vertical cryogenic mousehole and facilitates connection to the newly designed Pressure Core Analysis Transfer System (PCATS) for pressure core transfer and analysis, ensuring pressure containment is maintained without release.
3. Continuous monitoring of temperature and pressure profiles throughout the operation enables assessment of gas hydrate stability within the WPCS core, distinguishing between stable and dissolved states.  
Figure 1 illustrates the structural configuration of the WPCS, which was composed of an inner barrel assembly and an outer barrel assembly, viewing from the inside to the outside. The WPCS inner barrel assembly was suspended and connected inside the outer barrel assembly and integrated into the BHA (Bottom Hole Assembly) (The abbreviations are listed in Abbreviations). With an inner latch and outer latch, the inner barrel and outer barrel could be locked in place to cut and shape the core into the plastic liner with the cutting shoe during the coring process. The WPCS is segmented into three primary subassemblies from top to bottom: (1) the wireline running tool and the wireline retrieving tool; (2) the upper barrel system composing the outer latch barrel and the inner latch barrel; (3) the lower barrel system housing the pressure chamber section with an internal plastic core liner. Notably, the upper and lower barrel systems are interconnected via a quick link system, facilitating efficient assembly and disassembly within a vertical cryogenic mousehole to manage the temperature fluctuations. Additionally, the pressure chamber system is equipped with pressure and temperature recorders positioned atop the core liner. These instruments monitor and record the in situ pressure and temperature data throughout the coring recovery process.
## 4 Extension to Constrained Designs
### Design of False Target Delay Interval \(\Delta\tau\)  
In order to ensure the effect of jamming the radar, the delay interval \(\Delta\tau\) of false target jamming must be designed. If the density of the false target is too large, the coherence and power of each false target signal will be reduced by superimposing each other, adversely affecting the interference effect.  
From Equation (18), the greater the time delay, the greater the jamming matching filtering gain-loss ratio. The time delays for the positive and negative shifts of the target left and right are \(\tau_{1}=(1-\eta_{1})T_{r}/n^{2}\) and \(\tau_{2}=(1-\eta_{2})T_{r}/(n(n-1))\); the matching loss is greater for the overrunning false target compared with the lagging false target. The maximum time delays of the target's left and right positive and negative shifts are assumed to be \(\tau_{\text{1 max}}\) and \(\tau_{\text  
The delay interval \(\Delta\tau\) can be viewed as the communication element width, which affects the maximum communication rate of the joint system:  
\[R_{b}=\frac{1}{\Delta\tau\cdot M}\cdot n_{bit} \tag{19}\]  
where \(M\) is the number of pulse coherent accumulation of the intercepted radar. The digital modulator obtains the joint signal that maps the communication data to complex symbols according to the adopted QQAM modulation scheme, where a single symbol can carry \(n_{bit}\) data. The smaller \(\Delta\tau\) is, the better the communication rate is.
## 4 Case Results and Discussion
### Optimization Results and Discussion
Figure 4: Convergence of the proposed hierarchical model. Figure 6 shows the demand response values and the power demand before and after planning. Figure 6 illustrates that the proposed DCIM facilitates load shaving and valley filling. Demand-side energy storage is charged at night to accommodate excess wind power in the system, and it is discharged during the day to reduce the use of high-carbon-intensity power. Thus, the overall carbon emission of the system decreases. The 24-h load carbon emission responsibility map of Cases 0 and 1 are shown in Figure 7. It is obvious from Figure 7 that the overall responsibility of load carbon emissions in Case 1 is smaller than in Case 0, especially during the daytime. This is because the planned energy storage discharges the stored low-carbon power during high-carbon intensity, as described above. Therefore, demand-side energy storage planned under the proposed DCIM can facilitate the accommodation of wind power and carbon reduction.
## 2 Motor Imagery Classification Pipelines
### Pre-Processing  
Initially, the task of filtering is to prepare the recorded signals for processing by enhancing the SNR. In most studies, the simultaneous electro-oculography (EOG), electromyography (EMG) and electro-cardiography (ECG) is used, to exclude irrelevant signals. In addition, band-pass filtering within specific frequency bands is employed. However, this processing is challenging, since each subject expresses motor imagery ERD's and ERS's in varying frequency patterns [33]. Other filtering methods involve principal components analysis (PCA) and independent component analysis (ICA), which separate artifacts from the EEG signals either by excluding correlated activity or by transforming EEG signals into temporal and spatial independent components, respectively [34]. A simpler approach is to subtract common activity from the position of interest with common average reference (CAR) [35]. Lastly, spatial filtering with Laplacian filters is robust against the artifacts generated at regions that are not covered by an electrode cap, and it solves the \"electrode reference problem\" [36].
## 4 Overview of the Proposed Error Recovery Mechanism
### The Proposed Error Recovery Mechanism  
The strengths of NC and the weaknesses of the existing approaches, especially MP's [13] (summarized in Table 5), are the main motivation of the proposed error recovery mechanism. In order to improve MP's approach in terms of QoS performance, two QoS mechanisms are employed by this work: adaptive service differentiation and adaptive error recovery, which are briefly presented in the following subsections.