esa-sceva/satcom-chunk-collection huggingface dataset:
# Blind Compensation of \(I/q\) Impairments in Wireless Transceivers
## 3 Simulation Results
### Normalized Mean Squared Error and Image Suppression Evaluation
It can be seen that by using the proposed methodology the image has been suppressed down to the noise floor leading to a reasonable image suppression as presented in Table 2.  
Figure 7: Power spectral density (PSD) of WCDMA 1101 (S1, S2 and S3) signals before and after correction, using the proposed method for \(g\) = 0.92 dB, \(g^{\prime}\) = 0.5 dB and \(\theta\) = \(-6\) degrees, \(\theta^{\prime}\) = \(-2\) degrees under 20 dB SNR. Complexity: As mentioned earlier, the complexity of the proposed methodology depends on the optimization technique required to estimate the imbalance parameters. In the proposed work, we use simulated annealing algorithm as a case study to evaluate the speed and complexity of the proposed mitigation strategy. The number of data samples and the number of iterations required for convergence will be used as metrics to evaluate the speed of the algorithm. For a WCDMA 1111 signal, the proposed methodology requires as low as 3000 samples and 2820 iterations to converge to an NMSE of \(-19.37\) dB under 20 dB SNR. Similarly, for a 9 MHz LTE signal, with 3000 samples, the algorithm converges to an NMSE of \(-19.43\) dB in 2370 iterations.
# Post-Earthquake Fire Resistance in Structures: A Review of Current Research and Future Directions
## 7 Influence of Different Structural Systems
### Self-Centring Systems  
Self-centering systems are renowned for their superior seismic performance, often exhibiting minimal residual deformation after significant earthquakes. However, their behavior in subsequent post-earthquake fire (PEF) scenarios remains underexplored, necessitating further investigation to ensure these systems can withstand the compounded effects of multiple hazards [39]. One of the primary impacts of PEF on self-centering systems is the alteration of material properties at elevated temperatures. Earthquake-induced damage can influence the strength and ductility of steel when it is later subjected to high temperatures. Research indicates that the history of cyclic loading significantly affects the mechanical properties of mild steel--such as fracture elongation, elastic modulus, and ultimate strain--under these conditions. Although the number of loading cycles has a minimal effect, the amplitude of pre-strain substantially influences the high-temperature behavior of the material [38]. This finding underscores the importance of considering pre-damage effects when assessing the fire performance of self-centering systems [39].  
Another critical effect of PEF on self-centering systems is the geometric changes resulting from residual deformations caused by earthquakes. These deformations can lead to altered load paths and stress concentrations during fire exposure. For instance, studies have demonstrated that seismic events can significantly modify the geometric conditions of structures during subsequent fires [38], potentially reducing their fire resistance. The combined effects of these geometric alterations and changes in material properties due to PEF can lead to structural responses that differ markedly from those observed under fire-only conditions. Numerical simulations of steel self-centering moment-resisting frames (SC-MRFs) exposed to PEF have revealed larger inter-story drift ratios compared to those under fire-only scenarios [39]. This finding highlights the critical need to account for the combined effects of earthquakes and fire in the design of self-centering systems. The extent of pre-earthquake damage and the severity of the fire are crucial factors influencing the PEF behavior of self-centering systems. Generally, higher levels of pre-earthquake damage and more intense fire exposures result in more pronounced structural responses and an increased likelihood of failure. Moreover, various factors, including the type and arrangement of self-centering and energy-dissipating components, can significantly affect the PEF behavior of these systems. For example, SC-MRFs with different connection details--such as top-and-seat angles, post-tensioning strands, buckling-restrained plates, and pre-stressed bars--may exhibit varied responses under PEF conditions [38; 96]. Consequently, the specific configuration of the self-centering system must be carefully considered in the analysis to ensure optimal performance.
# The Power System and Microgrid Protection--A Review
## 3 Protection Scheme of Microgrid
### Harmonic Current  
The high penetration of converter-based distributed generation has increased harmonics levels in microgrid topologies. The power system harmonics depend on the topology of the network as well as the source of harmonic interactions [100; 101]. Modern relays can monitor total harmonic distortion (THD) of the inverter terminal, which can control the system concerning the harmonic changes. The harmonic content method is mainly designed to protect the DG rather than the power system [101]. The relationship between stability and harmonic importance of the microgrid has been studied in [102; 103]. The harmonic impedance is a key factor in assessing harmonic emission levels in electrical power systems. Reference [102] proposed a new measurement technique by a complex data-based least-square regression, combined with two techniques of data selection. The method takes into account two techniques of data selection. The two kinds of data-selection methods can be used together to cross-check the results of each other. The control strategies of inverter operation in a different mode has been suggested in Reference [103] in which each component of the microgrid harmonic is determined. In this method, the harmonic impedance is measured by injecting a current disturbance.  
[PERSON] and [PERSON] [104] presented a new type of protection scheme for microgrids based on the harmonics content of the inverter output voltage. Their method can protect against faults that are both internal and external to the protection zone. The method uses the Fourier transform (FFT) and THD. In Reference [105], a new high-frequency protection relay has been proposed to detect and isolate the fault by injecting harmonic signals. According to the strategy of harmonic injection, only one DG takes responsibility for the activation of the corresponding relay. As a result, the relays behave like directional relays. The main advantage of this method is that the resultant directional relays do not need any voltage transformer to detect direction of the fault. Therefore, it is a cost-effective solution for microgrid protection.  
Figure 13: Differential-based protection scheme.
# A Review of Artificial Intelligence Research in Peer-Reviewed Communication Journals
## 6 Conclusions  
Analyzing peer-reviewed journal articles related to AI research in the communication field can help scholars understand the stage of AI research, improve its scholarship, and outline directions for future research [35]. Thus, this study explored the trends in published articles on AI research within the communication field. It addressed key questions, including the main types of AI technologies studied, a comprehensive definition of AI in communication, and the authorial, topical, theoretical, and methodological aspects of AI research in peer-reviewed journals included in this study. Additionally, this study offered recommendations for advancing AI research in the communication field.  
The development of AI systems for the communication field has evolved from nascent technology to more sophisticated and advanced tools that have transformed communication strategies into more dynamic, responsive, and personalized ones. As the communication field adapts to this transition, understanding AI systems and their current challenges is significant for the field to leverage AI's potential [96]. AI's capability to analyze large datasets to understand the public's preferences, behaviors, and expectations is crucial to captivate and engage with effective communication technologies across various platforms. These opportunities have also created challenges for the field that need to be considered including ethical issues related to ensuring unbiased systems, protecting personal data as well as maintaining the accuracy of AI-generated information amidst the large amounts of data.  
This study was limited to articles published in peer-reviewed journals in the communication field found in the Web of Science database. Therefore, future research can extend the scope of research to trade press articles, books, book chapters, and conference proceedings. In addition, other peer-reviewed journals that are not in the Web of Sciencedatabase address AI from various perspectives should be included. Furthermore, one of the limitations of this review study that it did not include articles from 2023 and 2024, which overlooks advancements in AI during these years, including the development of advanced generative AI models and the emergence of new ethical and social challenges related to AI applications. As AI technology rapidly evolves, future reviews should consider extending the scope including articles from 2023 and 2024. This will ensure that the review reflects the most up-to-date information on AI trends. Despite these limitations, this study makes valuable contributions to AI research in the communication field. This study suggests using various methodological approaches, applying existing theoretical frameworks, or proposing new ones to continue investigating diverse topics across cultural and sociopolitical contexts.
## 5 Bonded Anchor Tests
### Time-Displacement Curves  
In the following chapter, typical time-displacement curves are shown for both products and all load levels. It is well known that the deformations in a bonded anchor system subjected to a constant load will increase with time due to the visco-elastic nature of both concrete and adhesive mortars. At higher load levels starting approximately at 30-40%, the development of damage will lead to an over-proportional increase in creep deformations with increasing load (nonlinear creep). As soon as cracks are formed, they propagate, leading to further stress redistribution, growing deformations, and ultimately progressive collapse. Clearly, the evolution of deformation rate and the failure time highly depend on the stress state. Typically, three different stages of creep can be identified: primary, secondary, and tertiary creep [19]. The first is characterized by quickly growing deformations, the second by a stable creep process with an almost constant creep rate while the third one is defined by progressively growing deformations that lead to failure; see [PERSON] et al. for a more detailed discussion [21]. Figure 6 shows typical time-displacement curves for both products, with time=0 after the load was fully applied.  
Both figures for P1 and P2 clearly exhibit different creep stages, where the duration and slope of the secondary creep phase is dependent on the load level/stress state. The duration of the secondary creep stage is related to the load level: the higher the load level, the shorter the secondary creep stage, and therefore the failure time. The trend among the curves is more apparent for Product 1 (Figure 6a), while the bands of experimental curves for Product 2 (Figure 6b) partly overlap. This can be explained by the higher scatter observed in the short-term tests of Product 2 which is (i) an indication of the larger inherent uncertainties, and (ii) is also propagated into the load level definition.
# Evolutionary Features for Dynamic Link Prediction in Social Networks
## 2 Dynamic Similarity Metrics
### Dynamic Features  
In this section, we describe methods to define dynamic features for the purpose of link prediction by considering the evolutionary aspects of non-connected actor pairs defined in the previous section. These features will denote the similarity of proximity between actors in regard to their structural and neighbourhood evolution. To define the similarity and/or proximity between actor pairs, we compare the time series information comprised of both structural and neighbourhood dynamicity, as calculated above, incident to actor pairs. For example, according to Figure 1, to predict a link between actors \(a_{1}\) and \(a_{2}\) in \(G_{4}\), this study builds two separate temporal sequences of \(\delta^{a}(t)\) and \(\lambda^{a}(t)\) (i.e., actor-level structural and neighbourhood dynamicity) incident to actor \(a\) and calculated using Equations (2) and (3) over time. For these two actors, the temporal sequences of structural dynamicity are \(a_{1}=[1,0.491,0.091]\) and \(a_{2}=[1,0.437,0.436]\). It is noteworthy that, for the first timestamp, the structural dynamicity is assigned to one since no variation can be computed using the first SIN. The proximity between a pair of actors is defined in regard to the temporal similarity and correlation between two different temporal sequences encompassing their dynamicity values over time. In the following sections, three different methods are described that are used to compute the similarity/proximity between actor-level evolutionary information for non-connected actor pairs in regard to their aforementioned dynamicity values. Each method assigns a score \(sim_{i}(a,b)\) to a pair of actors \((a,b)\) where the \(i\)th method computes the similarity or proximity between actors \(a\) and \(b\).
## 1 Introduction
In addition, the PDM can maintain the accuracy and consistency of the simulation even if the TP locations change excessively or increase in number. This paper presents a synthetic structural analysis method that can calculate the kinematic parameters of a structure by combining the DIP and PDM. We suggest two methods to improve the accuracy of the DIP: algorithms for the morphology operation and expanding target points. The noise around the target was reduced through morphology operation, and the convergence of accuracy was verified according to the extended TP. In addition, a method to improve the labeling error of the target was applied. Furthermore, we proposed a method to calculate the kinematic variables of a specimen undergoing deformation by combining the DIP and PDM, and a method to arrange the additional nodes and correct the radius of the influence domain to improve the accuracy of the derivative approximation when applying the PDM. Finally, we compared the synthetic structural analysis results with the actual experimental results obtained for a rubber beam specimen. Additionally, we compared these results with those of a numerical simulation by assuming the material to be elastic and only used the PDM to verify the accuracy and efficiency of the developed method. Note that the reason for selecting the rubber material for the three-point bending test was not to consider the nonlinear behavior of the material, but to present a solution to the labeling recognition error of the TP that could occur because of the excessive deformation of the specimen during the DIP.
## 2 Materials and Methods
### Satellite Data  
#### 2.3.1 Sentinel-2  
The Sentinel-2 Level-1C product was obtained from the Copernicus Open Access Hub ([[https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)]([https://scihub.copernicus.eu/](https://scihub.copernicus.eu/))) concurrent to the time of the field campaign (30 July 2016). To acquire Level-2A top of canopy reflectance (TOC) data, the image was atmospherically corrected using the Sen2 Cor processor. Sentinel-2 offers spectral data in 13 bands from 443 to 2190 nm with three red edge bands centred at 705 nm, 740 nm, and 783 nm, respectively. For this study, the 60 m resolution bands suitable for coastal (C, 443 nm), water vapour (WV, 1375 nm) and cirrus detection (CI, 1376) studies were not utilised. Reflectance spectra of the plots sampled in 2016 were extracted from the TOC data. The centre coordinates of the sample plots (20 \(\times\) 20 m) were overlaid on the Sentinel-2 image (20 m), and the reflectance of the corresponding pixels was extracted for each sample plot (Figure 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Measured Variables** & **Min** & **Mean** & **Max** & **StDev** \\ \hline LAI (m\({}^{2}\) m\({}^{-2}\)) in 2015 (n = 30) & 0.53 & 2.58 & 5.2 & 1.08 \\ LAI (m\({}^{2}\) m\({}^{-2}\)) in 2016 (n = 20). & 1.05 & 3.31 & 6.3 & 1.32 \\ Leaf dry mass (\(\mathrm{C_{m}}\)) (g cm\({}^{-2}\)) * & 0.005 & 0.0124 & 0.018 & 0.004 \\ Leaf water content (\(\mathrm{C_{w}}\)) (g cm\({}^{-2}\)) * & 0.007 & 0.014 & 0.022 & 0.0042 \\ Leaf chlorophyll content (\(\mathrm{C_{ab}}\)) (\(\mathrm{\SIUnitSymbolMicro g}\) cm\({}^{-2}\)) * & 13 & 30.4 & 49.5 & 10.2 \\ Average leaf angle (ALA) (degree) * & 45.3 & 56.7 & 73 & 8.1 \\ \hline \end{tabular}
* (n = 50 from 2015 & 2016).  
\end{table}
Table 1: Summary statistics of the field measured leaf area index (LAI) and other variables from the two field campaigns in Schiermonnikoog island, The Netherlands.
## 4 Simulations and Discussion
### Scenario 1: Approaching the Leader Car and Following the Leader  
In this scenario, it is assumed that the follower vehicle is far from the pilot vehicle, i.e., \(z^{L}(0)>>z(0)\), and their speeds satisfy \(v^{L}(0)>v(0)\), but the follower vehicle's speed is less than the reference speed set by itself, i.e., \(v(0)<v_{ref}\).  
Figure 3 shows the results of scenario 1. In the 0-5 s stage, the distance between the two vehicles gradually increases due to \(v^{L}(k)>z(k)\), and it should be pointed out that the speed of the vehicle remains within the preset range. During the 5-11.5 s stage, the distance  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** \\ \hline m/kg & 1300 & \(\rho\)/(kg/m\({}^{3}\)) & 1.3 & \(C_{r}\) & 0.01 \\ A/m\({}^{2}\) & 2.4 & \(\theta(k)\) & 2 & \(C_{d}\) & 0.32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of vehicle.  
between the two vehicles gradually decreases due to \(v^{L}(k)<z(k)\). After 11.5 s, the follower vehicle enters the safe distance range, and the controller switches from speed-prescribed performance control to distance-prescribed performance control. The two vehicles operate within the preset safe distance range while their speeds gradually converge.  
**Remark 2**.: _It should be emphasized that thanks to the novel stability analysis method proposed in this article, the designed ACC makes switching between the speed and distance control of the vehicle very smooth._  
**Remark 3**.: _In the speed control stage, the controller can constrain the speed error within a preset range, thereby ensuring the smoothness of the vehicle operation. In the distance control stage, the controller can constrain the distance error within a predetermined range, thereby achieving collision avoidance._
# Fault Location Method of Multi-Terminal Transmission Line Based on Fault Branch Judgment Matrix
## 3 Traveling Wave Head Detection Algorithm Based on CEEMDAN-TEO  
When transmission lines are fault-located using the double-end ranging method, the fault signals transmitted to both ends of the line must be decomposed first. A typical Hilbert yellow transform for signal processing has been applied to the decomposition of fault-traveling waves in engineering. Therefore, some researchers have decomposed the faulty traveling wave signal using CEEMDAN, which solves the problems of modal mixing and endpoint effects. However, the method still leaves part of the noise signal, resulting in significant errors in the positioning results. On this basis, this paper proposes to improve the CEEMDAN algorithm by preprocessing the decoupled mode components, calculating the instantaneous energy spectrum using TEO for the decomposed high-frequency components, and determining the traveling wave head through the spike moments in the instantaneous energy spectrum to accurately calibrate the time when the faulty signal reaches the endpoint.
# Research on Co-Interactive Model Based on Knowledge Graph for Intent Detection and Slot Filling
## 2 Related Work
### Knowledge Graph-Based Models  
Combining knowledge graphs with intent detection and slot filling allows models to learn domain-specific relevant knowledge in advance and relies on this prior knowledge to improve training effectiveness. [PERSON] et al. (2018) addressed the challenge of multimodal named entity recognition difficulties in specific domains by proposing the Knowledge Graph Multimodal Named Entity Recognition (KGMNER) model, which enhances the performance in identifying domain-specific entities, abbreviations, or easily confused entities in short texts with missing contexts. [PERSON] et al. (2019) proposed the K-BERT model to address the problem of pretrained language models (such as BERT) underperforming in knowledge-driven tasks in specific domains. K-BERT enhances the performance of pretrained language models in specific domains by injecting triples from a knowledge graph. Additionally, it employs a soft position and visible matrix to overcome problems related to knowledge noise. Although the above model incorporates domain knowledge from the knowledge graph, it primarily models the knowledge graph separately from intent detection and slot filling, ignoring the interplay between the intent and slot.  
After comprehensively considering the research findings from the aforementioned literature, we adopted the approach from reference ([PERSON] et al., 2019) to align and inject entities from the utterance with those in the knowledge graph for intent detection and slot filling in the specific domain. On this basis, we employed the methodology from reference ([PERSON] et al., 2019) to jointly model intent detection and slot filling. Specifically, we effectively leveraged domain-specific professional knowledge to address the challenge of accurately identifying specific abbreviations or easily confused entities by incorporating triples from the knowledge graph into utterances. Simultaneously, we capture the bidirectional information flow between intent and slot information to enhance the overall performance of intent detection and slot filling in a specific domain by modelling the relationship between the two tasks through a joint model.