esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of the Development Status of eLoran Time Service System in China
## 6 Signal System  
The eLoran signal of the high-precision ground-based time service system adopts the standard Loran-C system, with a carrier frequency of 100 kHz.  
The transmitter uses Eurofix data link and \"9 th Pulse Modulation\" technology to modulate the encoded data information onto a standard Loran-C signal, forming an eLoran signal. Among them, the Eurofix data link is mainly used for modulating the time code, leap second, system time deviation, system state, and other data, while \"9 th Pulse Modulation\" is mainly used for modulating the differential enhancement and emergency communication data. A schematic diagram of the modulated pulse is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c} \hline Center frequency (kHz) & 100 \\ \hline Broadcasting control accuracy (ns) & 30 \\ \hline Peak effective power (kW) & 2000 \\ \hline Transmitter efficiency (\%) & 70\% \\ \hline Signal coverage (km) & 800 \\ \hline Timing accuracy (before differential) (\(\upmu\)s) & 1 \\ \hline Timing accuracy (after differential) (ns) & 100 \\ \hline \end{tabular}
\end{table}
Table 3: Main performance parameters of transmitter.  
Figure 7: Transmitter schematic diagram.
# Phase Noise Cancellation in Coherent Communication Systems Using a Radio Frequency Pilot Tone
## 5 Transmission Setup with RF Pilot Tone Scheme  
Figure 5 depicts a 28-Gsym/s QPSK coherent optical fiber transmission system using an RF pilot tone orthogonally polarized against the transmitted signals. The electrical data were converted into 28-Gsym/s QPSK signals using an in-phase and quadrature (I-Q) optical modulator. The modulated signals occupied one polarization state, and the RF pilot tone was transmitted in the orthogonal polarization state. The signals and the RF pilot tone are integrated into the fiber via a polarization beam combiner (PBC). At the receiver side, both the received signals and the RF pilot tone are mixed with the LO laser, respectively, and are then detected by balanced photodiodes after the 90\({}^{\circ}\) hybrid. After that, both the received signals and the RF pilot tone are digitized using 8-bit analogue-to-digital converters (ADCs) at 56 Gsym/s and are then equalized using the EDC module. The signals are further multiplied with a conjugate of the processed RF pilot tone to suppress the impact of both LPN and EEPN. The central wavelengths of both the transmitter and the local oscillator lasers are 1553.6 nm, and the fiber dispersion coefficient is 16 ps-nm\({}^{-1}\) km\({}^{-1}\). Here, we have neglected fiber attenuation,  
Figure 4: Schematic of the differential phase detection.  
Figure 3: The optimal step size parameter in the NLMS CPR for various effective linewidths.  
PMD, and Kerr nonlinearities [27; 28]. The EDC is implemented in the frequency domain [23]. The CPR is performed using the one-tap NLMS and DPD methods, respectively.
# Efficient Coverage Path Planning for a Drone in an Urban Environment
## 6 Performance Evaluation  
This section describes the experimental setting used to evaluate the performance of the proposed algorithm. Simulation results are presented to compare the baseline algorithm with variants of the SST algorithm. In addition, the impact of various parameters on the performance of the proposed algorithm is analyzed.
# Controlled Fault Current Interruption Scheme for Improved Fault Prediction Accuracy
## 3 Validation of Half-Cycle Elimination Prediction Algorithm
### Validation of Recorded Waveform Data  
The proposed algorithm was evaluated using recorded waveform data from a short-circuit fault incident in the China Southern Power Grid (CSG). Figure 7 demonstrates the prediction performance under 15 ms sampling conditions, where the predicted short-circuit current from the algorithm (blue solid line) shows strong agreement with the actual fault current (red solid line). The fault initiation occurred at \(t\) = 0 ms, with successful clearance achieved at 38.4 ms. Notably, the computational error for the fourth current zero-crossing point post-fault was measured at 0.077 ms. This precision satisfies the accuracy requirements specified in the CFI guidelines for zero-crossing point determination, particularly in critical power system protection applications.
## 3 Results  
### Characteristics of the Sequencing Data  
The full-length 16S rRNA gene sequences were amplified from DNA extracted from bulk gut content, brood provision, and nest soil. A total of 376,787 raw sequences were obtained. After filtering low-quality sequences and removing chimeras, 373,858 Effective-CCS were achieved with an average length of 1436 \(\pm\) 18 bp (Supplementary Table S1). All sequences were clustered into 656 OTUs with a 97% identity threshold and further annotated to obtain 19 phyla, 30 classes, 76 orders, 111 families, and 217 genera (Supplementary Table S2). The gut, brood provision, and nest soil identified 156, 280, and 366 OTUs, respectively. The accumulation curve gradually reached a plateau with increased sequencing samples, indicating that the sample size was qualified for subsequent analysis (Supplementary Figure S1).
## 1 Introduction
All data between sensors and external sensor applications must pass through the sink node. Moreover, the sink node is quite free for the limitation of computational and energy resources comparing to ordinary sensors. We paid attention to this feature and we anticipated that a proper caching scheme on the sink node could improve the reusability of sensing data and provide a sophisticated data access. For the node-centric communication, TinyONet prepares a bypass method with which sensor applications can detour TinyONet and directly communicate to physical sensors. We implemented TinyONet on an embedded Linux machine and made it cooperate with TinyOS-based sensors. If a new kind of data access service is needed, the service can be dynamically injected into a running sink node without disturbing other parts of the system. The remainder of this paper is divided into five sections. Section 2 briefly analyzes the pros and cons of existing research and considers design principles. Section 3 describes a conceptual design, overall architecture, and basic ideas of TinyONet. Section 4 shows the implementation and operations of TinyONet. In Section 5, we evaluate TinyONet with the considered design principles and analyze the performance results. Finally, Section 6 concludes the paper.
## 1 Introduction
Considering DoS attacks prevent the information transmission between DGs, an acknowledgment (ACK)-based monitoring mechanism is developed to ensure successful communications once the sleep period of attack is detected. The main contributions of this paper are summarized as follows: (1) A two-layer secondary control scheme is proposed, where a state observer with ternary self-triggered control law is constructed and average voltages of DGs can be restored to their nominal values while maintaining accurate reactive power sharing. The communication burden between agents by our method can be highly reduced than that of the periodic way. (2) A class of time-sequence-based aperiodic DoS attacks is considered, an ACK-based monitoring mechanism is introduced to check whether the sleep period of DoS attacks has arrived. A new triggering condition is developed according to the monitoring results which ensures successful information exchanges when the sleep period is detected. (3) The stability of the proposed control strategy is proved using the Lyapunov method. The consensus of agents can be reached regardless of the frequency of DoS attacks which guarantees the achievement of secondary control objectives. Finally, simulation and experimental results are presented to verify the effectiveness and resilience of the proposed control scheme.
## 7 The Transformative Potential of Blockchain and Integration of AI in Education
### Challenges and Future Directions  
These integrated applications of blockchain technology have the potential to address key challenges in the Australian education system, such as credential fraud, personalisation of learning, and the need for more flexible, industry-aligned skill acquisition. However, realising this potential will require overcoming significant challenges.  
**Technological Infrastructure and Expertise**  
Developing the necessary technological infrastructure and expertise remains a key hurdle [51]. There is a shortage of educators and administrators with the necessary expertise to manage and implement blockchain technology effectively [38; 44; 46].  
**Regulatory Challenges, Privacy, and Data Protection**  
Evolving regulations may impact blockchain implementation. Compliance with local and international regulations will be crucial for successful implementation [14; 21; 40]. Creating supportive regulatory frameworks will be essential to navigate the complex landscape of education and technology [11; 35; 49].  
Addressing privacy and data protection concerns is crucial for widespread adoption [7; 24]. Ensuring that student data remain secure and private is paramount.  
**Environmental Impact**  
The power consumption associated with blockchain systems, particularly those using proof-of-work consensus mechanisms, raises concerns about their environmental sustainability [11; 40]. Mitigating the environmental impact of blockchain technology is a critical consideration for sustainable implementation. A mindmap presented in Figure 7 illustrates the intricate relationships between AI and blockchain technologies in the educational context. It expands upon the concepts discussed earlier, providing a more comprehensive view of how these technologies interact and influence various aspects of the learning process. The diagram highlights not only the core components and processes but also the far-reaching benefits and future implications of integrating AI and blockchain in education. While blockchain offers immense potential in education, the limitations identified including skill shortage, regulatory challenges, and environmental impact need to be addressed to ensure its successful implementation. Future research should focus on overcoming these obstacles, particularly by developing blockchain-friendly regulatory frameworks and addressing the technological infrastructure needed to support widespread adoption. Additionally, exploring the integration of blockchain with emerging technologies like AI could open new research pathways, ensuring that blockchain's role in education continues to evolve alongside other advancements.
## 3 Results and Discussion
### Effect of the Media Loading Evaluated from PEPT Experiments
Three dimensionless radial positions (r/D) were considered in Figure 16: r/D = 0.2 which corresponds to a position before the impeller tip, r/D = 0.3 which corresponds to the impeller tip position and r/D = 0.4 which is a position beyond the impeller tip. Figure 16 a) shows the comparison between DEM simulations (continuous line) and PEPT measurements (empty squares) for the impeller speed of 300 rpm. As expected, the velocity was maximum near the tips of the impeller arms and it was minimum in proximity of the wall where the friction and the high bead-bead contacts reduced the velocity. Both techniques suggest that the velocity profile could be described by three distinct zones: in zone 1, which extends from 0 < (Z/H) < 0.2, the velocity sharply increases; in zone 2, which extends from 0.2 < (Z/H) < 0.6, the velocity reaches a plateau and remains fairly constant; in zone 3, beyond Z/H=0.6, the velocity starts to decrease. The bottom pin, located exactly at Z/H equal to 0.2, was responsible for the sharp velocity increase in zone 1, whereas the rest of the pins ensured the steadiness of the motions in zone 2. Then, the velocity decreases in zone 3 beyond the top pin because the beads' motion is not directly driven by the impeller, but instead it is controlled by the gravity force and bead-bead friction. The velocity profile substantially changed for the impeller speed of 600 rpm, as shown in Figure 16b). For this impeller speed, the velocity plateau of zone 2 was smaller and zone 3 was larger  
Figure 16: Velocity magnitude along the dimensionless attriter height (Z/H) for three dimensionless radial positions (r/D: 0.2, 0.3, 0.4). Comparison between DEM results and PEPT data at the impeller speed of (a) 300 rpm and (b) at 600 rpm. compared to the impeller speed of 300 rpm. The increase in size of the latter region was due to the bed expansion as previously also described in Figures 4 and 5. The DEM predictions were in excellent agreement with the PEPT measurements except for a small area of the vessel close to the wall (r/D = 0.4) where for both the impeller speeds the simulations overestimated the speed by about 10%. This small difference in velocity can be caused either by deficiencies of the PEPT technique or by the limitations of using an oversimplified validation procedure based on just one parameter, the static friction coefficient.
## 2 The Peak Shrinking and Interpolation (PSI) Technique  
Although most of the recent research into PAPR reduction has concentrated on modified SLM and PTS methods, the most practical technique is a combination of clipping and filtering. The goal of of designing peak shrinking and interpolation (PSI) is to have a technique as practical as clipping, and as specific as windowing, an intermediate technique between windowing and clipping techniques. The idea is to simply find problematic peaks in power domain (time domain), extract them, shrink them, and put them back in their place.  
A general block diagram for the PSI technique is illustrated in Figure 5. It is seen that first the signal is generated, the PSI technique splits the signal into a number of slices (\(N_{s}\)), as shown in Figure 5.
## 3 Real-Coding PBIL Algorithm for Multi-Satellite Scheduling
### Value Probability Matrix  
In the real-coding PBIL algorithm, the probability is in the form of a matrix. The initial probability matrix is shown in Figure 3.  
A column of the probability matrix corresponds to  
After the optimal individual \(A\) is generated, the update formula of the probability \(P\) is as shown in Equation (7).  
\[P_{\mathrm{ij}}\!\!=P_{\mathrm{ij}}\!+\!X(X\;\mathrm{is\;a\;constant},\;\mathrm{ and}\;j=A_{i}) \tag{7}\]  
Obviously, the probability of the \(j\)-th value of the \(i\)-th gene position is increased. In order to keep the sum of the probabilities of all the values of the \(i\)-th gene position as 1, it is necessary to normalize the probability matrix. The normalization formula is as shown in Equation (8).  
\[P_{\mathrm{ij}}\!\!=P_{\mathrm{ij}}/\;(X+1)\;(1\;\leq\;j\;\leq\;n_{i}) \tag{8}\]  
Take the probability matrix update of the second generation as an example. The update of the probability matrix is shown in Figure 4.  
As the evolution process progresses, the probability of each value will deviate differently from the initial probability. Additionally, the offspring generated according to the probability update will be more likely to be highly adaptable.
# Material Characterization for Reliable Resin Transfer Molding Process Simulation
## 2 Materials and Methods
### Experimental Setup  
The permeability tests were performed on a 1D linear flow test bench. Two different models were used to test glass and carbon reinforcements (test bench used for glass fibers is shown in Figure 1).  
In order to obtain accurate and reproducible results, permeability tests should be carried out with non-reactive fluids: synthetic oil 15W40 and water were used instead of the resin to measure the textile permeability of glass fiber and carbon fiber, respectively. The oil was selected for its controlled viscosity profile over a broad range of temperature, while water is an option in the case of shortage of textiles to be tested (giving the possibility of reuse). The mold cavity had a size of \(440\times 100\times 6\) mm [16; 23; 24; 25; 26]. A free length of 20 mm was left at the inlet and outlet on the two sides of the specimen, in order to initiate a stable flow front and prevent potentially dangerous migrations of the fluid to the vacuum apparatus. Thus, a buffer volume was created, which works additionally as a device to reduce pressure fluctuations at the vent. A Plexiglass cover was positioned on top of the mold and fixed using a set of screws; a sealing rubber assured contact between the two mold halves and avoided leakages. Inlet and outlet ports were connected to the top plate for ease of construction.  
The mold cavity thickness was chosen in accordance with literature investigations [23; 24] and should be considered as an optimum that minimizes two adverse effects. On the one hand, capillarity at the boundaries becomes significant in shallower cavities, resulting in an incorrect reading of the fluid flow; on the other hand, the thickness effects cannot be neglected if thicker cavities are used, thus invalidating the assumption of planar flow.  
An adequate stack of textiles should be used to fill the cavity and realize the same fiber volume content as that on the part to be manufactured. Care must be taken when cutting the textiles to avoid wrinkles or voids at the edges of the mold cavity, as these would affect the planar fluid flow. Three repetitions were taken for each direction, i.e., \(0^{\circ}\), \(45^{\circ}\), and \(90^{\circ}\) orientations, for a total of 9 trials, for each material. A camera was used to monitor the injection process, and time was measured with a chronometer.  
Figure 1: Linear flow test bench used for measurement of glass textile permeability. The oil flow front is clearly visible in the detailed view.
# Secure Transmission for Buffer-Aided Relay Networks in the Internet of Things
## 4 Performance Analysis
### Average Secrecy Throughput and End to End Delay  
The average secrecy throughput can measure the average rate of the transmitted information which is kept confidential to the eavesdropper. Resorting to the work in [24; 41], the average secrecy throughput can be expressed as  
\[\bar{T}^{\star}=\frac{R_{s}}{2}\left(1-P_{out}^{\star}\left(\gamma_{th}\right) \right), \tag{33}\]  
where the factor \(1/2\) is because every packet reaches the controller takes two time slots.  
Recalling the definition of the secrecy outage probability, the value of \(P_{out}^{\star}\) is increased with the increase of \(R_{s}\). Thus, from Equation (33), we observe that the function of the average secrecy throughput with respect to \(R_{s}\) is a unimodal function. When \(R_{s}\) is small or large, only the lower average secrecy throughput can be obtained. That is to say, there exists an optimal secrecy rate which can maximum the average secrecy throughput of the considered system. The optimization problem can be given by  
\[\max_{R_{s}}\bar{T}^{\star}. \tag{34}\]  
Following a similar approach, we find the optimal \(R_{s}\) by utilizing the grid-search or the straightforward search techniques.  
In the buffer-aided relay IoT network, the end-to-end delay is the time slots it takes for a data packet to arrive at the controller from the source sensor, which is given by  
\[\bar{D}_{total}^{\star}=1+\bar{D}_{R}^{\star}, \tag{35}\]  
where the term \"\(1\)\" represents the delay at the source sensor. This is because each packet takes only one time slot when it is sent from the source sensor to the relay sensor. \(\bar{D}_{R}^{\star}\) denotes the average delay at the intermediate relay sensors. On the other hand, considering the probability of selecting a relay sensor \(R_{m}\) among all \(M\) relays is the same, we can obtain \(\bar{D}_{R_{m}}^{\star}=\bar{D}_{R}^{\star}\) and \(\bar{T}_{m}^{\star}=\bar{T}^{\star}/M\), where \(\bar{D}_{R_{m}}^{\star}\) denotes the delay at relay \(R_{m}\), and \(\bar{T}_{m}^{\star}\) represents the average secrecy throughput at relay \(R_{m}\).  
Then, we denote \(\varphi_{n}\left(m\right)\) as the queuing length in the buffer of relay \(R_{m}\) at state \(s_{n}\). Therefore, considering all of the possible states, the average queuing length at \(R_{m}\) can be written as  
\[\bar{Q}_{m}^{\star}=\sum\limits_{n=1}^{N}\pi_{n}^{\star}\varphi_{n}\left(m\right). \tag{36}\]  
With the help of the Little's law [42], the average delay at relay \(R_{m}\) is given by  
\[\bar{D}_{R_{m}}^{\star}=\frac{\bar{Q}_{m}^{\star}}{\bar{T}_{m}^{\star}}. \tag{37}\]  
Finally, based on the analysis above, the average end-to-end delay can be expressed as  
\[\bar{D}_{total}^{\star}=1+\frac{2M\sum\limits_{n=1}^{N}\pi_{n}^{\star}\varphi _{n}\left(m\right)}{R_{s}\left(1-P_{out}^{\star}\left(\gamma_{th}\right) \right)}. \tag{38}\]
## 6 Results and Discussion
### Results

Table 4: Characterization of individual disturbance factors introduced by each mediation technology. Figure 11: Comparison of mean scores from the user questionnaire feedback for the pick and place in the box task and 3D path following task, Likert scale: one to seven. Q1: I visualized the workspace (without any difficulties/with difficulties); Q2: Was the task tiring? (Not tiring/Very tiring); Q3: I managed to manipulate objects quite accurately (Not at all/Very much); Q4: The workspace visualization did not difficult object manipulation (Disagree/Agree); Q5: I forgot that I used an indirect technological visualization device (Disagree/Agree); Q6: I had a clear perception and total control of stick’s movements? (Not at all/Yes totally); Q7: I perform better when: (I move my head/I do not move my head); Q8: I know where the objects are because I can touch them. (Disagree/Agree). Q1: \(F_{3,60}=0.7\), \(p=0.541\); Q2: \(F_{3,60}=0.044\), \(p=0.987\); Q3: \(F_{3,59}=5.7\), \(p=0.0016^{*}\); Q4: \(F_{3,60}=4.04\), \(p=0.010^{*}\); Q5: \(F_{3,59}=4.23\), \(p=0.0088^{*}\); Q6: \(F_{3,60}=2.49\), \(p=0.068\); Q7: \(F_{3,38}=6.99\), \(p=0.0007^{*}\); Q8: \(F_{3,58}=0.64\), \(p=0.59\);  
Given that all of the 21 participants performing Task 2 filled in the questionnaires and just a part of them performed Task 3, we opted to detail Task 2. Figure 11 (red and blue bars) summarizes the obtained scores for each question while performing Task 2 with both interfaces. These results were validated through one-way repeated measures ANOVA analysis, and the statistically significant scores are marked with an \({}^{*}\) :  
Q1: \(F_{1,20}=2.016\), \(p=0.171\); Q2: \(F_{1,20}=0.022\), \(p=0.883\); Q3: \(F_{1,20}=11.294\), \(p=0.003^{*}\); Q4: \(F_{1,20}=6.981\), \(p=0.015^{*}\); Q5: \(F_{1,20}=26.544\), \(p<0.001^{*}\); Q6: \(F_{1,20}=2.016\), \(p=0.171\); Q7: \(F_{1,25}=8.432\), \(p=0.007^{*}\); Q8: \(F_{1,19}=0.516\), \(p=0.481\);