esa-sceva/satcom-chunk-collection huggingface dataset:
# CYK Parsing over Distributed Representations
## 2 Related Work
[22] introduced a formalism called recurrent neural network grammars, which is a probabilistic generative model based on CFGs, in which rewriting is parameterized using recurrent neural networks that condition on the entire syntactic derivation history. The authors introduced learning algorithms for recurrent neural network grammars and developed a parser incorporating top-down information. All of the above approaches use a mix of symbolic methods and distributed representations obtained by means of neural networks. One notable exception to this trend can be found in [23], where parsing was viewed as a sequence-to-sequence problem. The authors represented parse trees in a linearized form and exploited a long short-term memory network, which first encodes the input string \(w\) into an internal representation \(h_{w}\) and then uses an attention mechanism over \(w\) to decode \(h_{w}\) into a linearized tree for \(w\). This approach is entirely based on distributed representation and matrix multiplication; no auxiliary symbolic data structure is used. Despite the fact that Vinyals et al. [23] achieved state-of-the-art performance in natural language parsing, there is still a theoretical issue with the use of recurrent neural networks for context-free language parsing. Weiss et al. [24] and Suzgun et al. [25] showed that a long short-term memory network can process languages structurally richer than regular languages, but cannot achieve the full generative power of the class of context-free languages. In contrast with the hybrid architectures presented above, our proposal is entirely based on a neural network architecture and on a distributed representation, entirely giving up the use of auxiliary discrete data structures and symbolic representations. Furthermore, in contrast with the sequence-to-sequence approach, our proposal is a faithful implementation of the CYK algorithm and can in principle achieve the full generative power of the class of context-free languages.
# Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes
## 6 Conclusions  
In this paper, we proposed an efficient machine learning algorithm selection recommender system for agricultural data. The system is a promising new model for helping researchers select suitable machine learning algorithms for their agricultural data. The system is based on rule-based algorithms, which can handle categorical and numerical data and are less sensitive to outliers and missing values than other classification algorithms. These algorithms work well with diverse data types and can handle missing values, making them suitable for various agricultural data challenges. After individual machine learning processing, the proposed system was evaluated on a dataset of integrated new datasets. The results show that the system can accurately predict the best machine learning algorithm and data structure for crop data. Specifically, the proposed model achieved accuracies of 89.38%, 87.61%, and 84.27% for the decision tree, random forest, and random tree algorithms, respectively. These results are significantly better than those achieved by probabilistic and regression-based methods. Probabilistic and regression-based methods perform poorly with these agricultural datasets due to factors like highly correlated features and imbalanced data.  
In future work, we will explore integrating explainable AI (XAI) methods to provide insights into model predictions, to enhance the accuracy and efficiency of predictions further. This includes the potential adaptation of deep learning models that can process and analyze even larger datasets with higher dimensionalities. In addition to the scalability of the proposed models to larger, multi-regional datasets, it could help in understanding the broader applicability and limitations of the current approach. By addressing these areas, future research can not only enhance the technical capabilities of AI models in agriculture but also ensure that such innovations are accessible, practical, and beneficial at the ground level, thereby transforming agricultural practices sustainably and efficiently.  
E.E., N.M., C.Z., Z.A., A.E.T., and L.S. were involved in the whole process of producing this paper, including conceptualization, methodology, modeling, validation, visualization, and manuscript preparation. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are openly available in the Kaggle Dataset web link provided at reference number [35; 36; 37].  
The authors declare no conflicts of interest.
# Victim Detection and Localization in Emergencies
## 7 Results
### Localization Degradation under Rubble  
Figure 5 represents the Cumulative Distribution Function (CDF) of the localization error of the victim using the ranges analyzed in Section 7. The positioning error of the measurements under rubble (green dashed line) were compared with those of indoor localization (black line) [13] with the fusion of UWB and WiFi in both. As expected, it can be observed that the positioning accuracy worsened with the obstacles. The pink line represents the 80 th percentile, and the error for this point was augmented by 14 m when the victim was under rubble.  
Figure 4: Ranging error of all the reference points with the UE under rubble (blue) and outside the rubble (red).  
Figure 5: Localization performance under rubble (green) and outside of rubble (black).
# Design of Mega-Constellations for Global Uniform Coverage with Inter-Satellite Links
## 3 ISLs Design
### Intersatellite Links Design  
The highly dynamic nature of LEO satellites makes frequent switching of the earth-satellite and inter-satellite links inevitable. Link switching poses many negative effects on inter-satellite data transmission. For one, the large amount of pathfinding information generated by link switching is easy to cause network congestion. Secondly, the high latency of satellite rerouting and the high rate of data loss significantly reduce the utilization of the network. Therefore, it makes sense to prevent link switching. In this section, based on the mega-constellation configuration proposed in this paper, a solution is presented for designing the ISLs without inter-satellite link switching.  
Assuming that \(l\) ISLs are established on each satellite, the procedures of ISLs design is as follows:  
Step1: Referring to Section 2.6, calculate the maximum distance between any two satellites in the mega-constellation. Then, construct a matrix \(D_{max}\) of the maximum distances between satellites.  
\[D_{max}=\left[\begin{array}{cccc}0&d_{max}^{1,2}&\cdots&d_{max}^{1,N_{s}}\\ 0&0&\cdots&d_{max}^{2,N_{s}}\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\cdots&d_{max}^{N_{s}-1,N_{s}}\\ 0&0&\cdots&0\end{array}\right]\]  
where \(d_{max}^{i,j}\) is the maximum distance between the \(i\)-th satellite and the \(j\)-th satellite.  
Step2: The elements in the matrix \(D_{max}\) that do not meet the visible condition are denoted as 0, and they form a new matrix \(D_{max}^{\prime}\).  
Step3: Establish an adjacency matrix \(A\), which contains the ISLs information between satellites. The initial state of \(A\) is a zero matrix of order \(N_{s}\times N_{s}\). \(i=1\).  
Step4: Starting with the \(i\)-th row of \(D_{max}^{\prime}\), sort the row data in descending order. Record the location information of the first \(l\) smallest element in \(D_{max}^{\prime}\). Then, the elements in the corresponding position of \(A\) are assigned to 1, and the symmetric position of these elements in \(A\) is also set to 1.  
Step5: Calculate the sum of rows and columns of matrix \(A\). The sum of the elements in the \(j\)-th row or the \(k\)-th column is denoted as \(Row_{j}\) and \(Col_{k}\), respectively. If \(Row_{j}=l\) or \(Col_{k}=l\), the \(j\)-th satellite or the \(k\)-th satellite will no longer participate in subsequent calculations.  
Step6: if \(i<N_{s}\), then, \(i=i+1\), and repeat Step4 and Step5. Otherwise, terminate the loop.  
Step7: Output the adjacency matrix \(A\) that contains ISLs information.  
The above ISLs design steps produce a network where each satellite has a fixed ISL to the other \(l\) satellites. Each ISL in the mega-constellation is capable of ensuring full-time-domain interconnection. This stable link formation allows data to be transmitted over the satellite without switching links.
# Fine-Grained Fault Sensitivity Analysis of Vision Transformers Under Soft Errors
## 3 Results
### Results and Analysis  
#### 3.2.1 Model-Wise  
In our model-wise experiments, we considered a range of BERs from \(10^{-8}\) to \(10^{-2}\) for fault injection across all possible layers, using the first injection method, as mentioned in Section 3.1. Figure 4 illustrates the impact of BERs on classification error for different ViT models. The x-axis represents the BER on a logarithmic scale, while the y-axis represents the classification error. Each subplot corresponds to a different ViT model, showing how their classification performance degraded as the BER increased. A red dashed vertical line indicates the 0% accuracy loss BER threshold, which is defined as the highest BER at which the model's classification error is not higher than its original error. These results show that model size and patch size may influence the error resilience of ViT models. Different models' BER thresholds can span several orders of magnitude.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Preparation 1** & **Model-Wise** & **Layer-Wise** & **Type-Wise** & **Head-Wise** \\ \hline \(\approx\)0 h 2 & 10 h & 5 h & 2 h & 25 h \\ \hline \hline \end{tabular} 1 Preparation consists of recording original accuracy, weight quantization, and fault injection. 2 It is negligible compared to the execution time of other experiments.  
\end{table}
Table 4: Execution time required for preparation and different ViT fault injection experiments.  
Table 5 presents the accuracy loss across different models at various BER thresholds, specifically when the classification error loss reached 1%, 5%, and 10%. The general trend is that larger models with smaller patches tend to be more robust, while all models' errors are less robust. The results of the proposed method are shown in Table 6.  
Figure 4: Average prediction accuracy across all layers for different models and BERs: (**a**) B16-224-augreg (error bar up to 0.004367); (**b**) B16-384-augreg (error bar up to 0.003681); (**c**) B32\(-\)224\(-\)augreg (error bar up to 0.004277); (**d**) B32-384-augreg (error bar up to 0.002932); (**e**) L16-384-augreg (error bar up to 0.002641); (**f**) L32-384-orig (error bar up to 0.001566).  
increase significantly beyond a certain threshold of BER. These trends are consistent with the data collected through more than 70 h of neutron beam experiments [19].
## 7 Open Issues and Challenges
### Improvement in QoS  
WSN is the backbone of cutting-edge technologies like the Internet of Things (IoT) and the Internet of Everything (IoE), which rely on the quality of experience (QoE) and QoS as prerequisites. When choosing CH in WSN, several criteria like bandwidth, latency, end-to-end delay, throughput, and dependability are almost completely neglected. As a result, in cluster-based protocols for real-time IoT applications, these QoS characteristics must be taken into account [72].
## 4 Results and Discussion
### Influence of PMD, OSNR, and Nonlinear Effect on the CD Estimation Method
It can be seen from the figure that for the two types of signals, the larger input power (great nonlinear effect) in the optical fiber link will not have a great impact on the CD estimation results, and the corresponding maximum error varies within the range of (\(-100\), \(100\)) ps/nm. In order to further study the influence of the nonlinear effect on CD estimation, we calculate the FrFT value under the condition of different input fiber power at \(1000\) km, as shown in Figure 13. Whether for PM-QPSK or PM-16 QAM signals, a different input power will have different effects on the results of FrFT, and the CD value 16,000 ps/nm corresponding to the downward peak can still be clearly found in the figure. Therefore, it can be proved that the nonlinear effect will not affect the proposed CD estimation method.
## 4 Data Analysis
### Spatiotemporal Patterns  
The spatial layout of a city has a strong influence on movement patterns and social behaviors [32]. This section discusses spatiotemporal patterns and highlights how these patterns reflect the cultural and spatial characteristics of Beijing. Kernel density and cluster techniques were used to investigate how the usage patterns of Mobike as a feeder mode to rail transit are shared among rail transit stations and geographically distributed in the city.
## 4 Multihop D2D Broadcasting
### Resource Allocation in the Network
For instance, an eNB may inform its neighbor(s) about which RBs will be allocated to a cell-border P2 MP D2D transmission in a future TTI, so that the neighboring eNB(s) avoid allocating the same resources to UL or D2D transmissions in the vicinity of the cell border. This requires the sending eNB to plan scheduling on the SL (at least for cell-border UEs) with a lookahead of some TTIs, enough for the above message to reach its neighbor through the X2. With reference to Figure 7, at \(t=9\) the eNB informs its neighbor that, in a future TTI, a grant for a cell-border P2 MP D2D transmission will be scheduled on a given set of RBs. The receiving eNB marks the advertised RBs as occupied at the appointed TTI, and performs its usual scheduling. The lookahead mechanism can be expected to add a negligible delay to the broadcast diffusion, since the X2 connection is normally wired and low-delay. The eNBs should also select the MCS of P2 MP transmissions. Such choice should strike a tradeoff between two conflicting objectives, i.e., transmission range and resource consumption. In fact, selecting more performing MCSs implies reducing the number of RBs required for a transmission, since more bits will be packed in the same space. However, it will also decrease the transmission range, since the distance at which the SINR will be high enough to allow successful decoding decreases with the CQI. This implies that more hops will be required to cover a given target area. Conversely, selecting less performing MCSs will require fewer hops, but more RBs per transmission. Note that the eNB must choose the MCS only if it uses SRA allocation: in this case, in fact, the eNB sends D2D grants, which carry indication of the transmission format. If, instead, ARS is used, UEs may select the MCS autonomously, at least in principle. In practice, we argue that the eNB should still make that choice, and possibly advertise it periodically using RRC procedures. In fact, the eNB is in a better position  
Figure 6: Multicell scenario. UL: uplink. than single UEs to assess the UE density or location, hence to select the most suitable cell-wide transmission format.
# A 12-20 GHz Wideband High-Power SP2T Switch Based on Gap Waveguide Technology
## 5 Conclusions  
This article investigated the design and fabrication of a wideband microwave SP2T switch based on groove gap waveguide technology for Ku-band applications. The wave leakage in the sliding part of the switch was suppressed by utilizing the contactless feature of the gap waveguide technology. Therefore, manufacturing and assembling challenges were handled, and the switch was fabricated using a low-cost CNC milling technique. The measured results show that the input return loss of the switch was less than \(-14\) dB and the insertion loss was better than \(0.13\) dB, while the isolation level was about \(70\) dB within the operating frequency bandwidth (\(12\)-\(20\) GHz).  
Conceptualization, A.J.A. and A.F.; methodology and design analysis, A.J.A. and A.F.; experiments and data acquisition, A.F. and D.Z.; measurement evaluation, A.J.A. and D.Z.; writing of the manuscript, A.J.A. and A.F.; Review and editing, A.J.A. and D.Z.; Project administration and funding acquisition, A.J.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported and funded by Kuwait University Research grant no. [EE03/20].  
Not applicable.
# Towards Strengthening the Resilience of IoV Networks--A Trust Management Perspective
## 3 Trust Management in the Internet of Vehicles
### Trust Models
#### 3.2.1 Classification of Trust Models
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**No.** & **Trust Parameters** & **Contributions** \\ \hline
[25] & Similarity (SMR), familiarity (FMR), packet delivery ratio (PDR) & Five algorithms in machine learning were used to analyze similarity, familiarity, and packet delivery ratio. \\ \hline
[28] & Confidence, interactions & This paper proposed a V2X communication trust model based on a recommendation to resist internal attacks. \\ \hline
[29] & Confidence, time sliding window, and time decay function & The author proposed a trust evaluation and management model based on the perspective of historical interaction. \\ \hline
[30] & Detection rate & Evidence combination-based collaborative trust management scheme against attacks. \\ \hline
[31] & Inter-vehicular subjective trust weight, role-based trust weight, original trust of vehicle, neighbor trust calculation & Multi-level Hybrid Trust Management Model (HHTM). \\ \hline
[40] & Resource availability, trust score & The author proposed a cluster-based hybrid VANETs trust management scheme. \\ \hline
[35] & Location, time closeness, information quality, confidence & Context-based trust assessment and management framework. \\ \hline
[36] & Reward & Task-based experience reputation (TER) framework. \\ \hline
[37] & Co-location relationship, co-work relationship, frequency and duration, cooperativeness, reward system, mutuality and centrality, community of interest & A quantifiable trust evaluation mechanism based on machine learning. \\ \hline
[38] & Likelihood, impact & Risk-based trust evaluation advanced model (RTEAM). \\ \hline
[41] & Reward, loss & A heterogeneous blockchain-based Hierarchical Trust Evaluation strategy. \\ \hline
[42] & Attitude (AT), subjective norms (SN), perceived behavioral control (PBC) & The author put forward a trust evaluation model based on human psychology. \\ \hline
[43] & Development, loss, decay & An integrated trust model, called REK, takes the third party’s opinion, experience and direct observation as three trust indicators. \\ \hline
[44] & Friendship similarity, cooperativeness, co-work similarity, Community of Interest & The author proposed a time-aware trust model that utilizes social relationships. \\ \hline
[45] & Cooperation, freshness of data & Hybrid trust management mechanism based on communication and data. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A comparison of the existing state-of-the-art models.
# Gear Wear Process Monitoring Using a Sideband Estimator Based on Modulation Signal Bispectrum
## 4 Results and Discussion
### MSB-SE Characteristics of Gearbox Vibration Signals and Monitoring Indicator
Only peaks at (_nf\({}_{rx}\)_, _kf\({}_{my}\)_) (\(x\) = 1, 2, 3; \(y\) = 1, 2; \(n\), \(k\) are the order of shaft rotation frequency harmonic and the order of meshing frequency harmonic, respectively) will be selected with the gear operating time. Moreover, using MSB-SE slices for wear monitoring will reduce computational work significantly, and hence the method can be implemented in a reliable online real-time diagnostic system. As already shown, gear wear is an accumulation process; it will cause an ever-increasing change in the tooth surface from its initial state, and thus the gear state will change accordingly and can be reflected by certain gear vibration features [2]. Therefore, based on the MSB-SE slices peaks, the indicator is developed to characterise the dynamics of gear wear and hence to monitor gear vibration changes caused by accumulated gear wear, which is defined to be  
\[R(f_{r},f_{m},i)=\frac{\left|B_{\text{MS}}^{SE}(f_{r},f_{m})\right|_{i}}{ \left|B_{\text{MS}}^{SE}(f_{r},f_{m})\right|_{b}}\left(i=1,2,\cdots,T\right) \tag{7}\]  
where \(R(f_{r}\), \(f_{m}\), \(i)\) is the ratio of MSB-SE amplitude between the selected MSB-SE peaks at the measurement time \(i\) and that of baseline. For simplicity, \(R(f_{r},f_{m},i)\) is denoted as \(R\) in this paper. In general, the larger the value of \(R\), the more severe the resultant effect as gear wear is accumulated. However, as the wear accumulation may not cause the vibration deterioration (especially during the run-in period), it should be noted that \(R\) does not mean the volume of material removal from the gear tooth flanks with the wear accumulation. Therefore, if the baseline is relatively good, the trend of \(R\) would show the effect of gear wear severity on gear transmission dynamics directly. Furthermore, as \(R\) is a dimensionless parameter pertaining to individual gears, it could thus give a more reliable condition description of the individual gears.
# Storage and Processing of Big Data for Geomagnetic Support of Directional Drilling
###### Abstract  
Modern satellite positioning and navigation technologies are not applicable in specific areas such as the exploration of oil and gas deposits by means of directional drilling techniques. Here, we can rely solely on natural geophysical fields, such as the Earth's magnetic field. The precise underground navigation of borehole drilling instruments requires a seamless, near-real-time access to operational geomagnetic data. This paper describes the MAGNUS BD hardware-software system, deployed at the Geophysical Center of the Russian Academy of Sciences, that provides the efficient accumulation, storage, and processing of geomagnetic data. This system, based on the Big Data (BD) technology, is a modern successor of the MAGNUS processing software complex developed in 2016. MAGNUS BD represents one of the first cases of the BD technology's application to geomagnetic data. Its implementation provided a significant increase in the speed of information processing and allowed for the use of high-frequency geomagnetic satellite data and expanding the overall functionality of the system. During the MAGNUS BD system's deployment on a physically separate dedicated cluster, the existing classical database (DB) was migrated to the Arenadata database with full preservation of its functionality. This paper gives a brief analysis of the current problems of directional drilling geomagnetic support and outlines the possible solutions using the MAGNUS BD system.  
geomagnetic data applications; geomagnetic navigation; magnetic observatories; geomagnetic low-earth orbit satellites; database; Arenadata database; MAGNUS BD system +
Footnote †: journal: Applied sciences