esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of BDS-2 and BDS-3 Satellite Atomic Clock Products and Their Effects on Positioning
## 5 BDS-2 and BDS-3 ISB Analysis and PPP Solutions  
### Comparison of Receiver Clock Offset  
In order to further validate and analyze the ISB between BDS-2 and BDS-3, ionosphere-free PPP solutions have been processed based on observation from 10 stations. For this purpose, the BDS-2 and BDS-3 were regarded as two systems and their respective receiver clock offset was estimated. The station coordinates were fixed by the weekly PPP solution. Two sets of results were obtained using precise products from GFZ and WHU, respectively.  
Figure 4: Difference of clock datum discrepancy between BDS-2 and BDS-3 in different products. Comparison results of GFZ–WHU (**top**), BRDC–GFZ (**middle**) and BRDC–WHU (**bottom**) are shown with a sampling interval of 30 s, where the satellite PCO effect in BRDC is deduced for BDS-2.  
In addition, standard point positioning (SPP) solutions were also calculated with broadcast ephemeris. All solution processes were restarted at the day boundaries.  
Figure 5 shows the daily average values of the difference of receiver clock offset for BDS-2 and BDS-3 from MJD 58815 to MJD 59214. The MAD method is applied to detect gross errors. Three sets of results obtained with GFZ, WHU and broadcast products are depicted in red, blue and green, respectively. Overall, the receiver clock offset difference exhibits great stability across different days. In addition, the hardware delay varies between different receivers, but it is similar for receivers of the same type [44]. Due to the effect of different receiver hardware delays, there are significant differences between the results of some stations. For instance, the difference values of receiver clock offset with GFZ precise products fluctuate around \(-3.06\) ns for SGOC station, while fluctuating around \(3.73\) ns for XIA1 station. The average values of the daily average difference for each station together with the receiver type are summarized in Table 3. Moreover, the difference between the results with different products listed in the last column for an example, which is not affected by the receiver hardware delay, can greatly match respective difference of the clock datum discrepancy between BDS-2 and BDS-3 with a mean value of \(3.30\) ns as discussed in Section 4.2.  
In order to analyze the daily variation of ISB, the time series of receiver clock offset difference over a 3-day period (MJD 58999-59001) are depicted in Figure 6. For the time series with precise products, the difference values jump and re-converge at the day boundaries due to the restart of PPP solution processes. After convergence, the difference seems to be a constant value. In addition, some jumps are caused by data interruption. Similarly, the results with broadcast ephemeris are more dispersed and have no obvious jump because the precision is poor for SPP solutions.  
Figure 5: Difference between the receiver clock offset for BDS-2 and BDS-3. Average values per day for 10 tracking stations are shown. The color indicates which products have been used to process (red—GFZ; blue—WHU; green—BRDC).
## 1 Introduction
### Tracking Network  
The BETS network with BeiDou and GPS capacity has been deployed for scientific Positioning, Navigation and Time (PNT) service purposes. Since March 2011, 14 stations have already been established in China and its neighboring regions. Among these, 13 stations are employed in this contribution, eight of them located inside of China and five overseas. The stations in China are CENT in Wuhan, CHDU in Chengdu, HRBN in Harbin, HKTU at Hong Kong, NTSC and XIAN at Xi'an city, SHAO in Shanghai, and LASA in Tibet. The five overseas stations are SIGP (Singapore), PETH (Australia), DHAB (the United Arab Emirates), LEID (Netherlands), and JOHA (South Africa). The station distribution is shown in Figure 1. All the stations are equipped with the UR240 dual-frequency and GPS/BeiDou dual-system receivers and the UA240 antennas manufactured by the UNICORE Company in China [10]. As this is a newly developed receiver, some built-in attributes of the receiver antennas are unknown, for example, phase center offset (PCO) and phase center variation (PCV).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Satellite** & **PRN** & **NORAD-ID** & **COSPAR-ID** & **Launch Date** &
\begin{tabular}{c} **Mean Longitude** \\ **and Inclination** \\ \end{tabular} \\ \hline G1 & C01 & 36287 & 2010-001A & 16/01/2010 & 140.0 € \\ G2 & C02 & 34779 & 2009-017A & 14/04/2009 & Drift \\ G3 & C03 & 36590 & 2010-024A & 02/06/2010 & 80.0 € \\ G4 & C04 & 37210 & 2010-057A & 31/10/2010 & 160.0 € \\ G5 & C05 & 38091 & 2012-008A & 14/02/2012 & 58.75 € \\ G6 & unknown & 38953 & 2012-059A & 25/10/2012 & 110.5 € \\ \hline I1 & C06 & 36828 & 2010-036A & 31/07/2010 & 122 € (55 ) \\ I2 & C07 & 37256 & 2010-068A & 17/12/2010 & 119 € (55 ) \\ I3 & C08 & 37384 & 2011-013A & 09/04/2011 & 120 € (55 ) \\ I4 & C09 & 37763 & 2011-038A & 26/07/2011 & 96.5 € (55 ) \\ I5 & C10 & 37948 & 2011-073A & 01/12/2011 & 92.5 € (55 ) \\ \hline M1 & C30 & 31115 & 2007-011A & 13/04/2007 & Discarded \\ M3 & C11 & 38250 & 2012-018A & 29/04/2012 & 55 \\ M4 & C12 & 38251 & 2012-018B & 29/04/2012 & 55 \\ M5 & C13 & 38774 & 2012-050A & 18/09/2012 & 55 \\ M6 & C14 & 38775 & 2012-050B & 18/09/2012 & 55 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Satellites of the current BeiDou constellation.
## 3 Results and Discussions
### SEM Analysis  
SEM images were used to investigate the effect of XG on the soil microstructures (Figure 11).  
The untreated soil has a separate, flaky, and discontinuous structure. Larger voids with more conspicuous clumps of soil particles are observed (Figure 11a,b).  
The soil particles are coated and bonded with XG biopolymer. The pore fills with XG making an attachment of soil particles via the hard and firm biopolymer monomers indicating strong soil-biopolymer interactions (Figure 11c,d).
# Compensation Observer-Based Adaptive Output Feedback Control for Multi-Agent Systems
## 5 Simulation Examples and a System Analysis of Control Strategies  
This section presents two cases to illustrate the feasibility of our design strategy using numerical examples and gear system models. The objective of this control strategy is to progressively align each subsystem's actual output \(y_{i}\) with its desired output \(y_{r}\) by utilizing an adaptive control law \(u_{i}(t)\), developed through a compensatory observer control scheme. In this context, the differential equation \(\ddot{y}_{r}+2\dot{y}_{r}+y_{r}=\sin t\) defines the optimal output \(y_{r}\).
## 1 Introduction
In addition, among the algorithms for structural health monitoring (SHM) to perform modal identification of structural systems, SSI is a reliable time-domain technique using extended observability matrices [19]. Numerous studies have specifically concentrated on realistic applications of SSI in recent years. The SSI-COV method uses the calculation of correlation function through the output data and then constructs a correlation matrix. The observability matrix can be obtained by using the singular value decomposition (SVD) of the correlation function matrix, and then the modal parameters can be estimated. In 1993, SSI-DATA was proposed, based on the concept of Kalman filter and space-vector projection [18]. Through the projected output matrix obtained by projecting the output vector of the future into the output vector space of the past, we substitute the projected output matrix into the original correlation function matrix. The modal parameters can be estimated from the observability matrix obtained by SVD of the projected matrix [20]. SSI-DATA is relatively complete in the derivation process under the signal length limitation of general response data, but there are some cases where the calculation efficiency is poor. In this study, we introduce correlation function calculations in the SSI-COV system matrix method into the SSI-DATA algorithm. Through the SVD of the improved projection matrix, low computational efficiency due to the large matrix dimension can be avoided. By extracting two predictive-state matrixes with recursive relationships from the same original predictive-state matrix, the efficiency of computation can be improved, and the step of reevaluating the predictive-state matrix at the next-time moment can then be omitted.
# Analysis of Numerical Artifacts Using Tetrahedral Meshes in Low Frequency Numerical Dosimetry
## 5 Numerical Results
### Human Head--Simplified 3D Model  
Several exposure scenarios were studied for the two simplified 3D head models, all leading to the same conclusions. Therefore, for the sake of shortness, only the exposure to a homogeneous magnetic flux density is presented. The value of 200 \(\upmu\)T, corresponding to the reference level for public exposure is considered [5]. In both axisymmetric and planar cases (Figure 4c,d, respectively), the magnetic flux density has only \(z\)-component.  
Three-dimensional models corresponding to the axisymmetric and planar cases were created with both voxel (1 mm and 0.5 mm side) and tetrahedral elements to perform 3D simulations. For a fair comparison, tetrahedral meshes are created using a uniform mesh size such that the average volume of tetrahedra was equal to 1 mm\({}^{3}\).  
Table 3 summarizes the results for the axisymmetric case. First, it is worth stressing that, for the symmetry of the problem, as in the case of the multilayered sphere, the only source of numerical artifacts is the stair-casing approximation. The maximum exposure is registered in the skull and the tetrahedral mesh makes it possible to determine the maximum exposure without filtering the raw data with a maximum deviation of 0.2% with respect to the reference solution.  
Different conclusions can be drawn for the planar case. In this case, in fact, the induced currents cross the tissue boundaries, hence, possible numerical artifacts are not limited only to staircasing. Field singularities and the contrast of conductivities between contiguous tissues cause numerical artifacts as well. For this reason, the results of the planar case summarized in Table 4 appear completely different. The maximum exposure is registered in the gray matter and, in this tissue the use of tetrahedral elements causes an underestimation of about 7%. In the same tissue, the underestimation caused by the use of voxel elements is even higher, up to 50% when the 99 th percentile is applied.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Tissue**} & **Analytical** & & **Voxel 1 mm** & & **Tetra** \\  & **Solution** & **Max** & **99.9 th** & **99 th** & **Max** \\  & **(mV/m)** & & **(E/E\({}_{\text{analytical}}\))** & **(E/E\({}_{\text{analytical}}\))** \\ \hline Skin & 16.38 & 1.1563 & 1.0534 & 0.9844 & 1.0051 \\ Fat & 14.37 & 1.1104 & 1.0804 & 1.0265 & 1.0050 \\ Muscle & 13.47 & 1.1344 & 1.0618 & 1.0114 & 1.0034 \\ Skull & 12.63 & 1.2132 & 1.1223 & 1.0192 & 1.0026 \\ Muscle & 11.11 & 1.1524 & 1.0910 & 1.0294 & 1.0041 \\ CSF & 10.40 & 1.1836 & 1.0916 & 1.0187 & 1.0051 \\ Brain & 9.78 & 1.2699 & 1.0971 & 0.9373 & 1.0022 \\ CSF & 4.76 & 1.1721 & 1.1001 & 1.0016 & 0.9988 \\ Brain & 4.12 & 1.2389 & 1.1284 & 0.9878 & 1.0015 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Deviation between analytical and computed induced electric field on tetrahedral and voxel-based mesh.
# Context-Driven Service Deployment Using Likelihood-Based Approach for Internet of Things Scenarios
## 4 Proposed Technique
### Changes in Distributed Service Registry
#### 4.4.2 Joining of a New Node  
A node (IoT device) can become a member of a context network at any time, depending on its service usage pattern. The inclusion of a node in a context network does not automatically mean that that node will becomes a participant among the shareholders of the distributed registry; participation depends on the necessity of the inclusion of the concerned node. If an existing shareholder has an exhausted/full cache or if an existing participator node is leaving the context network, the newly joined node may participate as a registry shareholder. The participation of a node as a shareholder in the distributed registry is therefore situation-aware:  
* Situation-I: if the cache of an existing shareholder is already full, a newly joined node can take some shares from it, preventing the necessary service information from being lost.
* Situation-II: if an existing node is leaving the system, the shares held by that leaving node can be transferred to the newly joined node.  
In both the above situations, middleware is used to execute the necessary modules.
## 4 Conclusions  
In this paper, the reflection of acoustic waves in a multilayer medium containing a bubbly liquid was studied by theoretical calculations and experimental measurements. On the premise that the bubble size is much smaller than the wavelength and the bubbles are uniformly distributed, an equivalent sound velocity equation was derived relating only to the gas content in the bubbly liquid. The reflection coefficient of acoustic waves in a multilayer media with a bubbly liquid interlayer was calculated. At the interface between the layers of pure water and bubbly water, the change of the reflection coefficient with gas content was examined. The calculation results showed that with increasing gas content the valley frequency interval of the reflection coefficient decreases, while the amplitude becomes higher without considering attenuation. Thus, a novel method was proposed to detect trace bubbles in gas-liquid mixed media by measuring the reflection coefficient. Finally, the experimental measurements, which mainly consider the acoustic reflection characteristics of the thin layer of bubbly liquid medium, were conducted to verify the theoretical calculation results. Both the calculated results and the measured results showed that the reflection coefficient is sensitive to the change of gas concentration when the gas content is very small. However, when the gas content is greater than 1%, the reflection coefficient is not sensitive to the change of the gas content, and thus it is difficult to measure the gas concentration accurately by acoustic reflection signal in this condition. The proposed method has potential applications in hydroacoustic physics, hydrocarbon resources exploration, and the prevention and diagnosis of decompression sickness.  
Conceptualization, Y.W. and D.C.; methodology, Y.W. and D.C.; validation, Y.W., D.C., X.C. and X.H.; formal analysis, Y.W.; investigation, Y.W.; resources, D.C.; data curation, Y.W. and X.C.; writing--original draft preparation, Y.W.; writing--review and editing, Y.W., D.C. and X.H.; visualization, X.H.; supervision, D.C.; project administration, D.C.; funding acquisition, D.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 42074174).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 12: The reflected signal spectrum under different electrolytic currents measured experimentally.
## 4 The Proposed Collaborative Weighted Clustering Protocol
### Dynamic Clustering Protocols  
It is obvious that if the cluster heads are fixed during the operation, the unlucky cluster heads would die quickly and end the limited lifetime of all nodes that belong to those clusters [54]. Thus, it is recommended to employ a dynamic clustering protocol, which has a mechanism for changing the cluster heads in each round to distribute the energy load evenly over the whole network.  
In this section, the basic operation steps and the selection algorithm of the cluster heads are introduced. A dynamic clustering operation is broken into several rounds. Each round begins with a cluster set-up phase, followed by a steady-transmission phase. The steady transmission phase is longer compared with the cluster set-up phase for minimizing overhead.  
In the cluster set-up phase, each node decides whether or not to become a cluster head for the current round. In this procedure, the popular algorithm named LEACH has been widely used in the past, in which a random number between 0 and 1 is chosen by a sensor node \(n\). If the number is less than a threshold \(T_{n}\), the node \(n\) would become a cluster head for the current round. This threshold is set as:  
\[T_{n}=\left\{\begin{array}{ll}\dfrac{P}{1-P[rm{mod}(1/P)]},&n\in G\\ 0,&otherwise\end{array}\right. \tag{12}\]  
where \(p\) is the desired percentage of cluster heads, \(r\) is the current round, and \(G\) is the set of nodes that have not been cluster heads in the last \(1/p\) rounds. During the first round, each node has a probability \(p\) of becoming a cluster head. Once a given node is selected as the cluster head, it cannot be a cluster head for the next \(1/p\) rounds. In this way, the energy load would distribute evenly among the sensors in each cluster and this would not drain the battery of a single sensor, as happens in static clustering.  
Once all the cluster heads are determined, they broadcast their statuses to the other sensors in the network. Each sensor node determines the cluster to which wants to belong by choosing the cluster head that requires the minimum communication energy.  
In the steady transmission phase, a TDMA scheme is employed in each cluster. More specifically, each cluster head creates a schedule for all the nodes in its cluster. When it comes to the time slot for a given node, this node is allowed to transmit its data to the cluster head, while all the other nodes are required to be silent. This allows the radio components of each non-cluster head node to be turned off at all times except during its own transmit time, thus minimizes the energy dissipation in the individual sensors. Once the cluster head has all the data from the nodes in its cluster, the cluster head would aggregate the data and then transmit the compressed data to the base station. This process may further reduce the power consumption of the whole network.
## 3 Results
### Bacterial Viability Evaluation of Mature Biofilm  
To evaluate the efficacy of the tested solutions as antib Biofilm agents, their influence on the viability of S. mutans cells in the mature biofilms was also measured by MTT assay (Figure 3). The preformed biofilm treated for 24 h with CHG and mouthwash formulation 1  
Figure 1: Biofilm prevention concentration (BPC) effect of mouthwash formulations. S. mutans was incubated in optimal conditions to develop biofilm, in presence of different chlorohexidine concentrations in the different formulations (shown on the \(y\)-axis). CV assay was used to determine the percentage of residual biofilm. Data are reported as residual biofilm percentage, compared to the control for each formulation. A box, a filled circle and a dotted circle indicate the BPC for CHG and formulations 1, 2 and 3, respectively.  
showed a drastic reduction in cell viability, even when using a 30 \(\upmu\)g/mL concentration, and an almost complete killing effect was observed when a concentration of 120 \(\upmu\)g/mL was reached. On the other hand, a concentration between 240 and 480 \(\upmu\)g/mL of formulation 2 was needed to obtain the same bactericidal effect. Again, formulation 3 seemed to be less effective in affecting cell viability in a mature biofilm since 6.3% of metabolically active cells was still retained at a final added concentration of 480 \(\upmu\)g/mL. However, the Student \(t\)-test revealed that no significant difference was observed between formulations 2 and 3. Conversely, they were significantly different from CHG and formulation 1, which was the only one showing results statistically comparable to the CHG control.  
Figure 3: MTT assay on mature _S. mutans_ biofilm treated with mouthwash formulations. The percentage of metabolically active cells retained in mature biofilm after 24 h incubation is shown. Data are reported as percentage of viable cells compared to the growth control for each formulation. Data comparable to the ones obtained with CHG reference compound, characterized by a \(p\) value < 0.05 according to the Student \(t\)-test, are highlighted with *.  
Figure 2: CV assay on mature _S. mutans_ biofilm treated with mouthwash formulations. The percentage of residual biofilm, measured after a 24 h treatment, is shown. Data are reported compared to the growth control for each formulation. Formulation 1 was the only one with a trend significantly similar to the one observed for CHG (_p_ < 0.05).
## 2 Related Works  
### Communication Performance  
Currently, the primary focus of research in enhancing the communication performance of permissioned blockchains is on optimizing the local blockchain architecture and improving the performance and scalability of Byzantine fault-tolerant consensus. For instance, [PERSON] et al. [6] proposed a new PBFT consensus algorithm, designing a reputation evaluation model to select more trustworthy nodes for participation in system consensus, thereby achieving role differentiation. By constructing a dual consensus layer structure, communication complexity is reduced, fault tolerance is improved, and scalability is enhanced. This development is particularly relevant to Hyperledger Fabric, which relies on consensus mechanisms such as PBFT for secure and efficient block validation. [PERSON] [7] introduced a fair proof-of-stake consensus protocol for block creation, which proposes a simplified block verification procedure to reduce the verification process of trusted blocks and transactions, lowering computational costs, accelerating processing time, and enhancing security and privacy. [PERSON] et al. [8] utilized CouchDB to improve the storage performance of heterogeneous medical data within the Fabric consortium blockchain. This enhancement allows the blockchain to support more complex and efficient query operations, ultimately optimizing the consortium blockchain system's overall throughput and latency. [PERSON] et al. [9] introduced the MITOSIS method, achieving low latency in permissioned blockchains through a sharding technique similar to cell mitosis, and concurrently ensuring high scalability via parallel processing. [PERSON] et al. [10] suggested the use of a novel threshold digital signature technique, based on NP-Hard problems, to enhance the consensus algorithm. They developed a new, highly efficient, and secure blockchain system based on this approach, representing a classic research methodology. [PERSON] et al. [11] introduced the LEAP method, which integrates edge data distribution with coalition formation games to dynamically adjust the correlations between clients and ESs, while rationally allocating communication resources to mitigate the straggler issue. [PERSON] et al. [12] introduced a blockchain and MADRL integrated framework, enabling IoT devices and UAVs to continuously adjust resource demands and pricing strategies, thereby reducing the computation costs of IoTDs while maximizing the utility of UAVs and enhancing overall network performance.  
However, compared to research on the data layer and consensus layer, the network layer, situated between the two, has received less attention. In fact, optimizing the network topology to enhance the transmission rate and reliability can significantly contribute to positive effects on the overall performance and stability of the blockchain.