esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Algorithm for De-Interleaving Staggered PRI Signals
## 2 Problem Formulation and Proposed Algorithm Description
### Sequence Retrieval Algorithm Based on Matched Filter
Screen the next pulse by the class-matched filter operation. According to Formula (22), \(x_{0}(m)\) is the sampling signal of the reference pulse \(t_{ref}\), \(\mathbf{X}(m)\) is the sampling signal of the pulse set \(x[\mathbf{p_{s}}]\), \(x[n_{e}]\) is determined by the class-matched filter operation; go to Step (v);
5. Update the PRI value based on the searched next pulse: \[q=\frac{n_{c}*q+\frac{x[n_{e}]-t_{ref}}{k_{c}}}{n_{c}+1}\] (25) where \(x[n_{e}]\) indicates the next pulse found. Update reference pulse, \(t_{ref}=x[n_{e}]\); update number of pulses, \(n_{c}=n_{c}+1\); initialization pulse interval, \(k_{c}=1\); go to Step (ii); continue searching for the next pulse of the new reference pulse;
6. If \(n_{c}\) exceeds \(N_{TH}\), end the pulse sequence search; else, \(i=i+1\). If \(i\) is greater than \((length(x[n])-N_{TH})\), end the pulse sequence search; otherwise, update reference pulse, \(t_{ref}=x[i]\); initialization \(n_{c}=1\), \(k_{c}=1\); go to Step (ii). The flow of the sequence retrieval algorithm incorporating matched filter theory is shown in Figure 3.  
Figure 3: The flow chart of the sequence retrieval algorithm based on matched filter theory.
# Group Authentication Scheme for Neighbourhood Area Networks (NANs) in Smart Grids
## 2 Group Authentication
### The Need for Group Authentication  
In a large-scale environment, such as the smart grid, network scalability and availability are two crucial design parameters for a secure scheme. To manage a large scale NAN, organising the NAN into groups of SMs is necessary to  
1. Localise the topology: the SMs are distributed geographically based on the location of the consumers. It is essential that these devices are within the radio range of each other to be able to communicate with each other. Therefore, it is necessary to form groups of SMs. Groups are typically formed with some nodes in each group having overlapping radio range with other group/s. Such overlap provides potential redundant paths, when necessary.
2. Balance workload of intermediate relay hosts: SMs communicate to an upstream data sink in the smart grid via an intermediate host, typically a SM performing the role of a group gateway. It is necessary to limit the processing and storage load on the intermediate host that does the forwarding of data to and from the SMs. Typically, the SMs are organised into groups and assigned gateways to communicate with.
3. Distribute the SMs into functional groups: from an electricity provider's perspective, it helps to organise different categories of users into different groups (domestic, commercial, industrial, essential service provider, _etc._) for purposes of estimating demand, geographically, validating the usage, and controlling theft. Often, domestic and non-domestic users are located geographically close and the need for creating groups of SMs for operational semantics is necessary.  
With the obvious need for grouping SMs in a smart grid, group management is a necessary function within the smart grid. Identification of a group member, and members' joining/leaving the group, are typical group management functions that require authentication. The authentication may be performed autonomously by the group head or by an upstream entity in the smart grid.  
Often, it is not cost-effective to operate the SMs in a single-hop topology (to the upstream gateway) and, therefore, a multi-hop topology is necessary. The group members need to be validated as part of the group. Such validated members can communicate between themselves, primarily for purposes of forwarding data to/from the group head (providing multiple redundant paths to reach the group head). In the following section, we review literature relating to group authentication.
## 3 Results and Discussion
### Odor Analysis of Characteristic Volatile Compounds of Tea Tree Leaves Under Magnesium Regulation  
The formation of odor characteristics of tea depends on the volatile compounds contained in tea, and different volatile compounds present different types of odor characteristics, and the intensity of odor characteristics is closely related to the content of volatile compounds [37; 38]. For example, 2-octen-4-one has fruity odor characteristics [39], heptyl formate has green odor characteristics [40], geraniol has floral odor characteristics [41], 2,2,6-trimethylcyclohexanone has pungent odor characteristics [42], (-)-limonene has woody odor characteristics [43], and D-pantolactone has burnt odor characteristics [44]. Further  
Figure 5: Transformation relationship of characteristic volatile compounds.  
analysis of the odor characteristics of the 15 characteristic volatiles in this study revealed (Figure 6A) that these 15 characteristic volatile compounds under magnesium regulation exhibited six main odor characteristics, namely fruity, green, floral, pungent, woody, and burnt. Among them, fruity was mainly contributed by 2-octen-4-one, cycloheptanol, t-D-limonene, and t-limonene; green was mainly contributed by heptyl formate, metaldehyde, and (E)-carvone oxide; floral was mainly contributed by t-geraniol, t-geranyl alcohol, and t-iso-geraniol; pungent was mainly contributed by 2,4-dimethyl-2-oxazoline-4-methanol and 2,2,6-trimethylcyclohexanone; and woody and burnt were mainly contributed by t-(-)-limonene and D-pantolactone(-), respectively. Further analysis of the intensity of the odor characteristics of tea leaves after magnesium regulation showed (Figure 6B) that, with the increase in magnesium concentration, the odor characteristics of fruity, green, floral, pungent, woody, and burnt in tea were all significantly enhanced, especially floral, fruity, and green. It was evident that magnesium was beneficial to improving the intensity of odor characteristics of tea.
## 2 Micro-Strip Patch Antenna and DR Process
### Micro-Strip Patch Antenna
Existing research has suggested ways to intensify the productivity of the antenna by modifying copious antenna components [35; 36], but there exists no rigorous deliberation in terms of the variation of the substrates thickness so far [7]. Such deliberation regarding the variations is essential because most fabrication of the substrate is manual. Also, the  
Figure 2: Micro-strip patch (MSP) antenna. (**a**) Composition of the MSP antenna; (**b**) parts of the patch and the substrate. interests in printable or wearable antenna applications make the deliberation of non-uniform surfaces valuable. Therefore, it is fairly necessary to assume that the substrate thickness varies; however, these additional concerns demand excessive growth in the number of corresponding responses and DVs. When the engineers decide to weight the additional intricacy, it is typical to encounter the curse of dimensionality, which demands the DR methods in the design process. Copious DVs result in the curse of dimensionality, which triggers unreliable data assessment in classification and function approximation [37]. Furthermore, the multivariate variables are strongly correlated and arouse the redundancy of the data. The higher redundancy imposes negative impacts on the estimation of the system performance because the inter-correlated data is probably required to represent properties already taken into account. To describe the redundancy, Entropy [38] is often exploited. The uncertainties of a certain random variable are measured by Entropy (\(H\)) exploiting a probability distribution [39]. Here, \(x\) referring to a random variable \(H\) is represented by the following:  
\[H_{X}=\sum_{i=1}^{m}p(x_{i})\log_{2}\frac{1}{p(x_{i})}, \tag{6}\]  
where \(p(x_{i})\) means the marginal probability of \(x\). According to the \(H\), the redundancy (\(R_{e}\)) is able to be re-written as follows:  
\[R_{e}=\log_{2}N-\sum_{i=1}^{n}p(x_{i})\log_{2}\frac{1}{p(x_{i})}, \tag{7}\]  
where \(\log_{2}N\) indicates the maximum \(H\) with the entire samples (\(N\)). In order to elevate the assessment accuracy of the system performance, the DR approach is conducted in this research. The DR technique is generally conducted by the main methods: feature extraction (FE) [40] and feature selection (FS) [41]. While FE transforms the initial features located in the higher dimension to lower-dimensional new features, FS takes the most substantial subgroup of the raw features. Details of the DR methods will be explained in the following sections.
# Ship Network Traffic Engineering Based on Reinforcement Learning
## 1 Introduction
Naval ship design is a complex and monumental task. [PERSON] et al. [10] used AI to design modular naval ships, and the results were accurate, efficient, and interaction-friendly. Advances in computer vision may replace navigators. [PERSON] and [PERSON] [11] surveyed 42 studies on autonomous ship driving and proposed that before artificial intelligence can replace navigators, aspects such as flexibility, interactive design, and safety management must be considered. [PERSON] and [PERSON] [12] used YOLOv8 to detect and identify ships and solve problems caused by their irregular shapes and complex backgrounds. In the field of underwater acoustic recognition, machine learning methods rely on large amounts of data to achieve high accuracy, whereas the usual data amount in this field is small. [PERSON] et al. [13] used the residual network ResNet to conduct ship research for underwater acoustics. This method is also applicable to sound recognition in other fields. [PERSON] and [PERSON] [14] presents a federated learning framework for collaborative cyber threat detection without compromising confidential data. Artificial intelligence has been widely used in the shipping industry but to a lesser extent in ship network design. In this study, we used reinforcement learning methods to conduct simulation research on traffic engineering for ship networks. We considered the many challenges faced by ship networks, including limited bandwidth, unstable network connections, high latency, and instruction priority. We focused on three aspects: traffic balancing [15], instruction priority, and complex network structure. We also used simulation experiments to explore reinforcement learning's effect on these scenarios. Our study results show that applying reinforcement learning to ship networks significantly improves the quality of service (QoS) [16]. Specifically, reinforcement learning optimized network traffic balance, prioritized instructions, and coped with complex network structures. By analyzing experimental data, we observed that network consumption was reduced by 9.1% under reinforcement learning. It achieved priority routing of high-importance instructions more effectively and reduced overall network utilization by about 17.55%. This research provides a useful reference for optimizing ship network performance. It also highlights the potential advantages of reinforcement learning for environmental challenges in maritime communication. Our findings have important practical significance for improving the robustness and efficiency of ship communication systems. Figure 3: Schematic diagram of reinforcement learning.
## 3 Results
### Ionospheric Changes Using LEO Satellite Measurements
The first satellite pass took place from 13\({}^{\circ}\)W to 35\({}^{\circ}\)W between 20.63 UT and 20.82 UT, a few minutes before GE (20.86 UT), where the \(N_{e}\) values were greater than 10% in the entire evaluated interval (from 70\({}^{\circ}\)S to 30\({}^{\circ}\)S). The second F18 pass occurred from 38\({}^{\circ}\)W to 61\({}^{\circ}\)W between 22.33 UT and 22.52 UT, minutes prior to P4 (22.79 UT). The minimum \(DN_{e}\) was \(-\)21% (\(-\)0.04 \(\times\) 10\({}^{5}\) el/cm\({}^{3}\)) around 48\({}^{\circ}\)S at 22.44 UT. Regarding the ionospheric fluctuations during PSE2022, we also used three F18 ascending trajectories (see Figure 8c,d). Similar to F18 measurements of PSE2018, we compare the ionospheric changes of the eclipse day of 2022 (DoY 120) with respect to the mean \(N_{e}\) of two previously selected geomagnetically quiet days (DoYs 115 and 125). Before GE (20.69 UT), the satellite passed from 29\({}^{\circ}\)W to 51\({}^{\circ}\)W, between 19.73 UT and 19.93 UT. \(N_{e}\) remained positive from \(\sim\)60\({}^{\circ}\)S to \(\sim\)43\({}^{\circ}\)S, its value reached \(\sim\)20% at \(\sim\)58\({}^{\circ}\)S. The \(DN_{e}\) values were less than 0% from 43\({}^{\circ}\)S to the North, with a minimum value equal to \(-\)10% (\(-\)0.02 \(\times\) 10\({}^{5}\) el/cm\({}^{3}\)) at \(\sim\)31\({}^{\circ}\)S. F18 traveled from 54\({}^{\circ}\)W to 77\({}^{\circ}\)W between 21.43 UT and 21.63 UT, about an hour after GE (20.69 UT), where the minimum \(DN_{e}\) was \(-\)54% (\(-\)0.07 \(\times\) 10\({}^{5}\) el/cm\({}^{3}\)) at 54\({}^{\circ}\)S. The third selected pass of this satellite took place from 79\({}^{\circ}\)W to 102\({}^{\circ}\)W, (23.13 to 23.33 UT), after P4 (22.63 UT). It had a minimum \(DN_{e}\) equal to \(-\)30% (\(-\)0.06 \(\times\) 10\({}^{5}\) el/cm\({}^{3}\)) at 40\({}^{\circ}\)S.  
Figure 7: Ionospheric behavior using SwA measurements for PSE2022. (**a**) VTEC data gathered through the satellite orbits taken by SwA at 850 km (400 km above SwA) are presented over the maps during the eclipse day (DoY 120) and the two selected geomagnetically quiet days (DoYs 115 and 125). The black line indicates the magnetic equator. (**b**) Comparison of the latitudinal VTEC profile on the eclipse day (red line) with the mean VTEC of two closest trajectories longitudinally and temporally (DoYs 115 and 125, green line) between 70\({}^{\circ}\)S and 0\({}^{\circ}\). (**c**) In situ \(N_{e}\) and (**d**) \(T_{e}\) measurements at 450 km are presented in the same way as VTEC data. The longitudes and time intervals of the satellite passes are also indicated.
## 5 Discussion
### Application Scenarios  
Blockchain technology produces a structure of data with inherent security qualities since it is based on cryptography. In most blockchain and distributed ledger applications, the data are structured into multiple blocks, and each block can be used to carry information. The encryption and protection on the blocks make our proposed design especially suitable for blockchain systems that transmit sensitive information, such as cloud computing and financial systems.  
Individuals and small companies tend to deploy their services on the cloud instead of purchasing and maintaining their own servers because of many benefits, including cost-saving, fast deployment, scalability, etc. However, confidentiality and integrity of the data in a remote cloud computing environment are some of the security concerns. If the cloud service, or a connected device, is breached, sensitive data could be accessed [30]. The data including personal health information, personally identifiable information, trade secrets, and intellectual property are often the targets of data breaches and require robust security protection in cloud computing [31]. Even though cloud computing and blockchain seem to be deceptively in conflict from the perspective of infrastructure that cloud computing is representative of centralized computing and the blockchain typically stands for decentralization, but there are many fields on which blockchain and cloud computing can be integrated [32]. For example, the storage of the cloud computing environment can be built based on blockchain with our design so that the stored data are split and individually encrypted. The advantage of our design over encryption on undivided data is risk diversification. Meanwhile, the inherent features of blockchain including parallel throughput bandwidth and data tamper resistance are also retained in our design.  
Blockchain technology has supported multiple cryptocurrency ecosystems and has inspired the interest of the financial sectors. The financial industry has already started the experiment with blockchain to see how the distributed ledger techniques can leverage financial transactions. However, due to the characteristics of sensitivity in the financial transactions, the blockchain system applied in the financial industry tends to be a private blockchain. In the private blockchain, only a few participants can read and write on the distributed ledger to prevent data leakage. With our design, the public blockchain can also be used to build the transaction systems based on blockchain such that the communication between nodes is split and encrypted, and the decryption can only occur when the number of recipients agreed on is more than a predefined threshold value.
## 3 Results  
### HPLC-MS/MS Method Validation  
#### 3.1.1 Linearity, LLOQ and Selectivity  
All the three calibration curves were linear in the selected concentrations range 0.3-150 ng/mL for Fingolimod and 1.5-150 ng/mL for Fingolimod-P; the determination coefficients (r\({}^{2}\)) were always >0.9955 for Fingolimod and >0.9943 for Fingolimod-P and the slopes were highly reproducible with a precision (CV%) less than 15% for both Finogilomod and Fingolimod-P calibration curves (Table 2). The accuracy and precision of the back-calculated concentrations were within the acceptance limits (Table 2).  
The peak area measured with the lowest Fingolimod and Fingolimod-P concentration (0.3 and 1.5 ng/mL, respectively) was 5-fold the signal measured in the blank human plasma (data not shown), prompting us to consider this concentration as the LLOQ for both analytes.  
Selectivity of the method was evaluated confirming that the interfering peaks in six independent plasma matrices, which co-eluted at the same retention time of Fingolimod and Fingolimod-P, were always \(\leq\)20% of the peak area of Fingolimod and Fingolimod-P at LLOQ level (0.3 and 1.5 ng/mL, respectively). Absence of interfering peaks in mobile phase solution, in blank plasma and in blank plasma spiked with IS was also assessed qualitatively (Figure 1). Similarly, interfering peaks which co-eluted at the same retention time of IS were always \(\leq\)5% of the peak area of IS at the concentration used (10 ng/mL) (data not shown).
## 3 Numerical Tests
### Couette Flow
#### 3.1.2 Effect of Scaling Factor  
In order to examine the effect of scaling factor on the prediction accuracy of PINN, five hard constraints with different magnitudes of \(a\) (i.e., \(a\) = 1.0 \(\times\) 10\({}^{-4}\), 1.0 \(\times\) 10\({}^{-2}\), 1.0, 1.0 \(\times\) 10\({}^{2}\) and 1.0 \(\times\) 10\({}^{4}\)) are formulated at the same degree of \(k\) = 2.0. The comparison of maximum streamwise velocity between the PINN prediction results and the analytic solutions at different Reynolds numbers is listed in Table 2. As can be seen, the PINN-predicted maximum streamwise velocities obviously deviate from the analytic solutions when \(a\) is equal to 1.0 \(\times\) 10\({}^{-4}\) and 1.0 \(\times\) 10\({}^{4}\) at all the examined Reynolds numbers, while the smaller relative errors of around 1.0% are observed when \(a\) varies from 1.0 \(\times\) 10\({}^{-2}\) to 1.0 \(\times\) 10\({}^{-2}\). The best prediction accuracy is achieved when \(a\) is equal to 1.0 within the range of the examined Reynolds numbers. In addition, if we compare the relative errors in Table 2 against those in Table 1, it is interesting to find that the relatively large errors with \(a\) = 1.0 \(\times\) 10\({}^{-4}\) and \(a\) = 1.0 \(\times\) 10\({}^{4}\) are still much smaller than those with the first-degree hard constraints. This indicates that the degree of the hard constraint is the most important parameter for the PINN prediction accuracy, followed by the scaling factor.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Degree** & **Reynolds Number** & **PINN-Predicted \(u_{\text{max}}\)** & **Analytic Solution of \(u_{\text{max}}\)** & **Error** \\ \hline \multirow{5}{*}{First} & 139 & \(-\)11.7739 & 0.4167 & 2924\% \\  & 313 & 15.8395 & 0.6250 & 2433\% \\  & 553 & \(-\)12.8592 & 0.8333 & 1369\% \\  & 1250 & 11.7101 & 1.2500 & 836\% \\  & 2000 & 17.3297 & 1.5811 & 996\% \\ \hline \multirow{5}{*}{Second} & 139 & 0.4167 & 0.4167 & 0.000\% \\  & 313 & 0.6244 & 0.6250 & 0.096\% \\ \cline{1-1}  & 553 & 0.8331 & 0.8333 & 0.024\% \\ \cline{1-1}  & 1250 & 1.2500 & 1.2500 & 0.000\% \\ \cline{1-1}  & 2000 & 1.6099 & 1.5811 & 1.822\% \\ \hline \multirow{5}{*}{Third} & 139 & 0.4087 & 0.4167 & 1.920\% \\ \cline{1-1}  & 313 & 0.6253 & 0.6250 & 0.048\% \\ \cline{1-1}  & 553 & 0.7215 & 0.8333 & 13.416\% \\ \cline{1-1}  & 1250 & 1.0540 & 1.2500 & 15.680\% \\ \cline{1-1}  & 2000 & 1.1914 & 1.5811 & 24.647\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the maximum axial velocity of Couette flow between analytic solutions and PINN-predicted results with different degrees of hard constraints.
## 2 Background and Related Work
### Modelling Obstructions
#### 2.1.3 Proposed Substitution of 2D Vector Files and Basic 3D Data  
In previous GNSS shadowing studies, the importance of using detailed, accurate obstruction models has been emphasised. For example; high accuracy models were used to mitigate multipath in a survey area, improving the navigation solution [27] by minimising the number of false signals reaching the receiver [28]. Fisheye and IR cameras can be used to remove non-visible satellites in the position calculation, through post-processing or even in real-time [29, 30] and these methods are independent of city models. When detailed obstruction models are employed, they are created through simulation [31] or by survey [32]. In [33], polyline shapefiles containing one-foot bare-earth elevation, contours were used and building roof and ridge outlines and elevations were manually digitized.  
However, due to the cost of surveying an area, processing the data and creating the models, there is a strong justification for developing a method that can take advantage of existing national mapping such as Ordnance Survey Ireland (OSi) 2D digital vector mapping and any available elevation information. This type of mapping is available to most government agencies and other public bodies and is therefore a ready source of mapping for GNSS predictions. It is one of the goals of this paper to demonstrate that the proposed method of converting 2D vector mapping and basic elevation data into a raster DSM is a practical alternative to surveying the area. Basic elevation data can be acquired from low density aerial surveys, satellite imagery or alternatively, information on building heights available from Town Planning Department or Land Registry databases usually reserved for planning appeals, property registration or \"right to light\" appeals. This combination of 2D polygons and sparse elevation data yielded promising results when applied in [34] to facilitate automatic extraction of objects from LiDAR data. The proposed methodology will be employed to avoid the need for costly, time-consuming surveys and intensive computer processing.