esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 A Brief Overview of AHL Systems in _Burkholderia_
### Other Pathogenic Burkholderia spp  
There are some human and phytopathogens, including phytopathogenic _B. glumae_, _B. gladioli_ and _B. plantarii_ that do not cluster with either the BCC or with the _B. pseudomallei_ groups. Quorum sensing is known to play an important role in the pathogenicity of these bacteria [64; 65; 66]. Typical  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Species** & **QS system** & **ID** & **Gene topology** & **Chr. No.** & **Major AHL** & **Comments** \\ \hline \multicolumn{1}{c}{} & & & & & & All plant-beneficial strains \\ \multicolumn{1}{c}{} & & & & & & & have this system. Ex: \\ \multicolumn{1}{c}{} & & & & & & & _B. phytamatum STMS15_, \\ \multicolumn{1}{c}{} & & & & & & _B. phytofirmans PsJN_, \\ \multicolumn{1}{c}{} & & & & & & _B. servoorans LB400_, \\ \multicolumn{1}{c}{} & & & & & & _B. graminis C4D1M_, \\ \multicolumn{1}{c}{} & & & & & & _B. unamae etc._ \\ \hline \multicolumn{1}{c}{} & & & & Chr1 & & Present in some plant- \\ \multicolumn{1}{c}{} & & & & & (_B. phytofirmans_ & & \\ \multicolumn{1}{c}{} & & & & & & _B. phytofirmans_ & \\ \multicolumn{1}{c}{} & & & & & & _PsJN);_ & & _B. phytofirmans PsJN_, \\ \multicolumn{1}{c}{} & & & & & & & _B. xenovorans LB400_, \\ \multicolumn{1}{c}{} & & & & & & & _B. graminis C4D1M_ \\ \hline \hline \end{tabular}
\end{table}
Table 6: QS topologies in plant-beneficial and environmental _Burkholderia_ sps.  
Figure 8: Chromosomal arrangement of QS genes (_bral/R_) and OriC in completely sequenced members of the plant-beneficial and environmental group (for example, _B. phytofirmans_ PsJN, _B. xenovorans_ LB400, _etc._).  
Figure 9: Regulatory circuit of _xenI2/R2_ and _bxeR_ genes in plant-beneficial and environmental group (for example, _B. xenovorans_ LB400) [63].  
arrangements are shown in Table 7. _B. glumae_ causes disease in rice seedlings and _B. gladioli_, which is phylogenetically close to _B. glumae_, is both a rice pathogen as well as an opportunistic pathogen in humans [65]. Another plant pathogen is _B. plantarii_ which also requires the AHL QS system for its pathogenicity [64]. Interestingly, _B. glumae_ has different topology of AHL QS genes in two different strains. _luxR_ and _luxI_ homologs in _B. glumae_ BGR1 are divergently transcribed and are separated by an intergenic gene _tofM_, which is an _rsaM_ homolog [31, 36, 67]. In contrast, the LuxR homolog in _B. glumae_ ATCC 33617 is 110 amino acids longer and is believed to be non-functional [65]. This _luxR_ homolog is also divergently transcribed with respect to its _luxI_ homolog but is not separated by any intergenic gene [65]. The topology of _B. gladioli_ is similar to _B. glumae_ BGR1. In _B. plantarii_, _luxR_ and _luxI_ homologs are divergently transcribed and are adjacent to each other.  
Regulation of the QS system in these species is canonical in that a positive feedback loop is present. The repressive action of TofM in _B. glumae_ BGR1 remains unclear and needs to be further studied [36].
# Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms
## 5 Conclusions  
We presented HTAPF, a novel fully decentralized algorithm that couples a bio-inspired task allocation algorithm with a distributed motion planner for persistent robotic service tasks. The task allocation component significantly extends our prior work by supporting multiple hierarchical decision levels and a utility function that directly considers the (estimated) cost of robot motions. The motion planning component is based on a distributed prioritized planning scheme with goal assignment within a specified region. Both approaches are tightly coupled because the motion planner's cost estimates are directly considered for area/task assignment. Tests on different environments show that the proposed solution performs well in all tested cases, validated by statistical tests and compared to two baseline algorithms. We also demonstrate that HTAPF is robust with respect to limited communication and induced robot failures. Although the HTAPF algorithm is characterized by several free parameters, no specific tuning has been performed for the experiments presented here. Proposed values are chosen according to previous work and reasonable heuristics, and have been kept fixed across different problem instances to demonstrate the wide applicability of the proposed approach. While improvements in performance are possible with a detailed tuning of all the parameters for specific experimental settings, these results demonstrate that the system properly generalizes to different conditions, different number of robots and different introduced limitations. Indeed, we have shown that performance is satisfactory also in those cases where the nature of the environment (e.g., robot failures or communication limitation) is not known in advance. We believe that the proposed solution is a good fit for many realistic service task scenarios or situations where robots with limited power, reliability, or communication are taken into account. Examples of the latter are unstructured environments that lack a central entity, a distributed network that covers the entire area,
# Asynchronous Quantum-Resistant Blockchain for Secure Intelligence Sharing
## 5 Experiments Result
### Computational Resource Usage  
In this subsection, we evaluated the computational resources by assessing the algorithms used, the time spent at each stage, and the differences in the number of \(M\) and \(B\), using a data size of 1 MB.  
The computational performance comparisons between SPHINCS+ and ECDSA in terms of key generation, signature, and verification under the configurations of sha128f, sha192f, sha256f, and haraka128f against the P-256 curve of ECDSA are shown in Figure 13. The table below the graph provides the specific times for each operation. Although SPHINCS+ exhibits slower performance in signing operations, its key generation and verification times are comparable to those of ECDSA. Moreover, SPHINCS+ offers the higher robustness against quantum attacks, ensuring a better level of protection. For a deep insight into the phase analysis, the time consumption at different stages is illustrated in Figure 14.  
Figure 12: Storage space cost for different blockchains.  
Figure 13: Computational performance of SPHINCS+ vs. ECDSA.  
The further-detailed computational resource usage in the sharing phase by the number of companies maintaining the blockchain is displayed in Figure 15. It is clear that the increasing number of companies has led to the linear increase of computational resources required for the sharing phase. The computational resource usage during the transactions phase relative to the number of transactions is depicted in Figure 16. As the number of transactions increases, resource usage shows a significant rise, highlighting the computational cost associated with the higher transaction volumes.  
These results have demonstrated a detailed analysis of the computational efficiency and resource requirements for different post-quantum cryptographic algorithms, evidencing the balance between security and performance in a blockchain environment.  
Figure 14: Computational resource usage across phases.  
Figure 15: Sharing phase resource usage by the number of companies.
# Stability Analysis of Lane-Keeping Assistance System for Trucks under Crosswind Conditions
## 4 Discussion  
This study focused on the use of a LKAS control strategy in crosswind environments to control vehicles to drive smoothly in the middle of the road lines on straight and curved composite roads. Through the test results, it can be seen that when the vehicle entered the curve at the 4 th second and encountered crosswind, the sideslip angle and lateral acceleration of the vehicle with LQR control were more stable and less fluctuating than those without LQR control, and the yaw rate changed less and more smoothly with LQR control, which increased by 66.7% compared to without LQR control, thereby improving the lateral stability of the vehicle in the crosswind environment.  
However, due to the limitations of the conditions, there are still some shortcomings in the safety analysis of trucks, mainly because this study used step wind, whereas the  
Figure 11: Yaw rate.  
Figure 12: Desired and actual steering angles.  
actual crosswind environment is mostly unstable wind, the wind strength on the vehicle is different at different times and seasons, and there are environmental problems including rainy days, snow days, etc., which have a greater impact on the safety of trucks. In future experimental research, it is necessary to build a more reasonable, effective, and realistic simulation environment, conduct more detailed experiments, and analyze the results, to further improve the theoretical system.
## 5 Numerical Modeling
### Results of the Numerical Study and Discussion
#### 5.3.4 Effect of Welding of the Flange Plates in the STS Structure on Ground Settlement.  
In their previous study, the authors have shown the importance of bottom flanges welding on the performance of STS structures through extensive experimental work [18; 19]. In the construction of the Northwest Street station, the flanges at the free face of the STS structure were welded timely after soil excavation as shown in Figure 30a. In this section, the effect of the welding of the flanges are investigated on the performance of the STS structure in the station construction using the numerical simulations. As discussed before and shown in Figure 30b, the STS structure is simulated as a composite  
Figure 29: The horizontal deformation of bridge piles after each construction stage: (a) Bridge pile1; (b) Bridge pile 2; (c) Bridge pile 3; (d) Bridge pile 4.  
of two different materials. The STS structure with or without welding of the flanges can be simulated by appropriately adjusting the material properties of the composite system as discussed earlier in the paper. The impact of welding of the flanges in the STS system on the ground displacement is studied by simulating different welding conditions for the upper left pilot tunnel (Z1).  
The settlement profile for the crown of Z1 and Z2 tunnels, and after the bottom (Z2) is completed, the ground with and without welding of the flanges are shown in Figures 31 and 32 for the section at y = 15 m. The ground settlement above the pilot tunnel Z1 increases around 1.3 mm with the welding of flanges during the excavation, and the ground settlement increases 1.35 mm without welding of the flange, increasing 5.47%. The crown settlement of the pilot tunnel Z1 also shows only a slight decrease (from 2.1 mm to 1.9 mm) when flanges are welded. It can be concluded that the welding of flanges during the excavation of the upper pilot tunnel Z1 has little impact on the settlement of the whole structure.  
Figure 31: Settlement profiles at a section with y = 15 m: (**a**) Ground settlement; (**b**) Vertical deformation of horizontal section of the STS structure.  
Figure 30: Welding of flanges in the STS structure: (**a**) Welding of flanges in the site; (**b**) Different welding conditions for the flanges in the simulations.
# A Decentralized Blockchain-Based Trust Management Framework for Vehicular Ad Hoc Networks
## 2 Methods
### Trust Metric Evaluation  
The first phase of the proposed framework is to compute local trust metrics for the vehicles saved locally in their monitors, these local trust metrics will be aggregated later into global trust metrics in the miners. To compute the local trust metrics of the vehicles in the proposed BC-TMF, protocol EDTCP proposed in [15] is used. It is a hybrid approach aiming to compute the trust metrics of the vehicles, and evaluate the reliability of the disseminated alerts. Each vehicle monitors its neighbors and updates their trust metrics accordingly. The local trust metric update is event-driven. Upon perceiving an event on the road, vehicle \(v\) periodically broadcasts alert messages that report the event in its neighborhood. Once vehicle \(m\) receives the alert message from vehicle \(v\), it should, first, check the credibility of the event reported in the alerts. To this end, a tier-based data reliability assessment technique is used [11]. It aims to decide if the detected event is reliable or not. Afterward, vehicle \(m\) updates the trust metric of vehicle \(v\) accordingly: if the event is reliable, \(Tm(v)\) increases by 0.1 otherwise it decreases by 0.1:  
\[Tm(v)=\left\{\begin{array}{c}+0.1,\text{if the event is reliable}\\ -0.1,\text{otherwise}\end{array}\right. \tag{1}\]  
Initially, vehicle \(v\) has \(Tm(v)=0.1\) in vehicle \(m\). Each neighbor \(m\) of vehicle \(v\) separately computes a trust metric for it, this local trust metric will be stored in a local database named List_Local_Tm(m). Consequently, vehicle \(v\) will have more than one local trust metric evaluated by different vehicles at the same time. Besides, vehicle \(v\) has to cooperate with its neighbors along its journey to increase its trust metrics stored locally in its neighbors. Let us note that all the vehicles mutually and independently monitor and compute trust metrics about each other. The next phase of BC-TMF is trust metrics aggregation.
## 2 Materials and Methods
### Analysis of nVOCs  
For the identification of nonvolatile organic compounds (nVOCs), 50 mg of the crude extracts were resuspended with 2 mL of methanol. All samples were filtered through 0.22 \(\upmu\)m nylon membrane prior to injection. The HPLC system used for the chromatographic determination was an Agilent 1200 (Agilent Technologies, Santa Clara, CA, USA) equipped with a vacuum degasser, autosampler, and binary pump. The column was a Gemini C18 (50 mm \(\times\) 2 mm, 100 A, and 3 \(\upmu\)m particle size; Phenomenex, Torrance, CA, USA).  
The binary mobile phases consisted of water (A) and acetonitrile (B) with 0.1% v/v formic acid. The initial gradient of the mobile phase was 5% B for 5 min and was increased to 95% B over 10 min. It was maintained at 95% B for 20 min, reduced to 5% B for 5 min, and then maintained for 5 min. The flow rate was maintained at 0.3 mL/min and 20 \(\upmu\)L of each sample was injected.  
Mass spectrometry (MS) analysis was performed using a Q-TOF-MS (6540 Agilent Ultra High Definition Accurate Mass, Santa Clara, CA, USA), equipped with an Agilent Dual Jet Stream electrospray ionization (Dual AJS ESI, Santa Clara, CA, USA) interface in positive and negative ion mode over the range of m/z 50-1500. The parameters were as follows: drying gas flow (N2), 8.0 L/min; nebulizer pressure, 30 psig; gas drying temperature, 350 \({}^{\circ}\)C; capillary voltage, 3.5 kV; and fragmentor voltage, 175 V. Targeted MS/MS experiments were carried out using collision energy values of 10, 20, and 40 eV. Integration and data elaboration were managed using MassHunter Qualitative Analysis software B.08.00 (Agilent, Santa Clara, CA, USA) [19]. The analysis was carried out in triplicate.
## 5 Evaluation
In real-life situations, activity labels that are similar in any two aspects will be regarded as redundant according to the majority voting mechanism. Because the data quality on the activity label is a unique problem in process mining, even though the redundant activity labels are similar in semantics, they still need to be regarded as similar in terms of the control-flow relation or the attribute value. Hence, we are more interested in evaluating whether our framework can detect redundant activity labels that are different in semantics, because the effectiveness of the first two features is critical in the proposed \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Event Log** & **Number of Traces** & \begin{tabular}{c} **Number of Trace** \\ **Variants** \\ \end{tabular} & **Number of Events** & \
\begin{tabular}{c} **Number of** \\ **Attributes** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Activity Labels** \\ \end{tabular} \\ \hline Hospital Billing & 100,000 & 1020 & 451,359 & 1105 & 18 \\ Sepsis & 1050 & 846 & 15,214 & 26 & 16 \\ Helpdesk & 4580 & 226 & 21,348 & 22 & 14 \\ BPI Challenge 2012 & 13,087 & 4366 & 262,200 & 69 & 24 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of event logs used for evaluations. framework. Therefore, we artificially renamed the activity labels arbitrarily following [11] and intentionally set the voting weight of semantic similarity to 0 when performing the experiments. Hence, activity labels that are similar in both two aspects were regarded as redundant in the experiments. We will further demonstrate the use of semantic similarity in the real-life case study proposed in Section 6. The proposed framework was implemented as a Python program for evaluations. We adopted \(\theta_{c}=0.25\) and \(\theta_{d}=0.1\) for each aspect.
## 4 Simulation Results
### Histogram  
The histogram shows the effectiveness and security of the encryption. The ideal histogram should be almost different from the original image. The histograms of original images are shown in Figure 7a-d, respectively. Figure 7e shows the histogram of the encrypted image, which is very different from the original image. Figure 7f shows the other encrypted image using different original images, which is similar to Figure 7e.
## 1 Introduction
Additionally, these studies have not explored the suitability of satellite and reanalysis precipitation estimates for identifying flood characteristics in hydrological simulations [44; 45]. Furthermore, the recently released ERA5 precipitation product has not been assessed for hydrological applications over the Pearl River Basin [46; 47]. From the perspective of flood modeling, it can generally be hard to conclude which of the existing precipitation products performs the best, as each has its own advantages. To improve the accuracy of precipitation estimates for hydrological applications, some studies have attempted to merge multi-source precipitation products [14; 48; 49; 50; 51]. However, almost all these related merging studies have focused on adjusting precipitation magnitudes and improving their temporal-spatial resolutions, instead of comparing the corrected satellite products with their uncorrected ones for better hydrological modeling [52]. Few works have investigated the possible ways to improve streamflow simulation by directly merging the modeled streamflow outputs of their corresponding precipitation inputs [48], while the state-of-the-art satellite and reanalysis precipitation datasets such as IMERG and ERA5 have not been taken into account. Overall, there is a prominent gap in the hydrology literature associated with comparing multiple-precipitation-input merging and streamflow-output merging for better flood modeling at individual catchments, which is a crucial motivation for the present research. To explore the above issues, this study comprehensively evaluated and investigated hydrological simulations of multiple uncorrected and corrected satellite and reanalysis precipitation products, by two merging strategies in the flood-prone Beijing and Dongjiang Rivers of the Pearl River Basin, China. The objectives of this study were two-fold: (1) to evaluate the hydrological utility of nine satellite and reanalysis precipitation products during 2006-2018 at a daily scale using the Coupled Routing and Excess Storage (CREST) hydrologic model, and (2) to develop and compare two merging schemes--(i) precipitation input merging and (ii) streamflow output merging--using the arithmetic mean method, to investigate their capabilities for improving streamflow and flood simulations. We seek deeper insights into the merging approaches for better flood modeling, and new findings regarding the uncertainties of global precipitation products are revealed and discussed. This study will provide valuable references for hydrological applications and also facilitate better flood modeling.
###### Abstract  
Transformer oil will inevitably be contaminated by impurity particles in the actual operation process; Carbon particles are the most abundant conductive particles in transformer oil. The adhesion behavior on the internal insulating surface will cause electric field distortion, which will pose a serious threat to the safe and stable operation of oil-immersed power equipment. To this end, this paper builds an experimental platform for simulating the adhesion behavior of carbon particles in transformer oil, studies the adhesion characteristics of carbon particles, and analyzes the influence of electric field, particle size, oil flow velocity, and other factors on the adhesion of carbon particles. The results show that: the DC electric field is the main factor driving the adhesion of carbon particles on the surface of epoxy resin (with the increase in electric field strength, the degree of adhesion of carbon particles firstly rises and then decreases); the smaller the size of the carbon particles, the easier it is to adhere, and the corresponding electric field strength is different for different sizes of carbon particles when the degree of adhesion is the largest; the velocity of the transformer oil will have a significant impact on the adhesion behavior of the carbon particles (with the increase in the flow velocity, the degree of adhesion of carbon particles firstly rises and then decreases). The research conclusion of this article is helpful in guiding the evaluation of insulation performance and the optimization of insulation structure design in the converter transformer valve-side bushing considering the phenomenon of particle adhesion.  
DC electric field; transformer oil; carbon particles; epoxy resin; adhesion characteristic
## 4 The Proposed Communication Strategy
### Clustering of EDs  
In our system, the central server not only collects the sensor data from the EDs, but it is also responsible of forming clusters of users in close proximity. This task is achieved assuming that the system is equipped with a localization system where the server is aware  
Figure 7: LoRaWAN Infrastructure together with Sensor Node shown in Figure 1.  
of users' locations. More specifically, the creation of clusters of users is performed with the aim of coordinating the transmissions of close users to avoid possible packets collisions. Indeed, it is highly probable that a critical event such as the presence of gas is jointly detected by all the EDs of the cluster.  
The clustering algorithm and the specific localization technology to be adopted in the system are still under investigation and are beyond the scope of this paper. Figure 8 depicts the overall vision of the system where several GWs are installed inside the service area and the clusters of users are associated to the closest GW (represented by different colors). In the figure DCP stands for downlink control packets, which are regularly transmitted by GWs to the associated EDs as detailed in the next section. Note that ED-GWs association is fully in charge of the server and is only to provide a separated control mechanisms, i.e., it is completely transparent to the EDs which are actually connected to each GW as in standard LoRaWAN.