esa-sceva/satcom-chunk-collection huggingface dataset:
# Candidate Term Boundary Conflict Reduction Method for Chinese Geological Text Segmentation
###### Abstract  
Though Chinese word segmentation (CWS) relies heavily on arithmetic power to train huge models and human work to label corpora, models and algorithms are still less accurate, especially for segmentation in a specific domain. In this study, a high-degree-of-freedom-priority candidate term boundary conflict reduction method (HFCR) is proposed to solve the problem of manually setting thresholds on segmentation based on information entropy. We quantify the uncertainty of left and right character connections of candidate terms and then arrange them in descending order for local comparisons to determine term boundaries. Dynamic numerical comparisons are adopted instead of setting a threshold manually and randomly. Experiments show that the average F1-value of CWS for Chinese geological text is higher than 95% and the F1-value for Chinese general datasets is higher than 87%. Compared with representative tokenizers and the SOTA model, our method performs better, which solves the term boundary conflict problem well and has excellent performance on single geological text segmentation without any samples or labels.  
Chinese word segmentation; information entropy; degree of freedom of terms; zero-sample; single geological text +
Footnote †: journal: Applied  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-  
continuously and manually. Therefore, CWS tasks need to be free from manual consumption and high-performance equipment.  
To address the above-mentioned issues, a high-degree-of-freedom-priority candidate term boundary conflict reduction method (HFCR) is proposed. The idea of this method is analogous to the process of human reading. A person who first starts reading a text without a knowledge background will gradually confirm word meanings and word groupings by gradually increasing the number of occurrences and contexts of proper nouns. Theoretically, the richer the left and right connections of a string and the more independently it can appear in the text, the higher the probability of the string becoming a term. We use information entropy to quantify the uncertainty of left and right character connections of candidate terms and to determine term boundaries. The experiments demonstrate that the HFCR algorithm achieves an average F1 value of more than 95% for single geological text. Compared with the representative tokenizer and SOTA, the HFCR algorithm has obvious advantages and achieves single geological text segmentation without any samples, labels, or high-performance equipment. At the same time, our algorithm also performs well in the general field.  
The rest of this article is organized as follows. Section 2 describes some related research work. Section 3 introduces our ideas and algorithm. Section 4 discusses the experimental data and results. Section 5 summarizes the advantages and disadvantages of the HFCR algorithm and puts forward suggestions for future research and improvement.
# Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications
## 4 Requirements of 6G
Moreover, it could be concluded that 6G will enhance the data rate, latency, spectrum, and connectivity density by up to 10-100 times compared to 5G. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristic** & **5G** & **6G** \\ \hline Operating frequency & 3 GHz–300 GHz & Up to 1 THz \\ \hline Peak data rate & 20 Gbps & 1 Tbps \\ \hline Latency & 1 ms & 10–100 \(\upmu\)s \\ \hline Mobility & 500 km/h & \textgreater{}1000 km/h \\ \hline Available spectrum & 30 GHz & 10–100 times higher than 5G \\ \hline Spectral efficiency & 30 bps/Hz & 100 bps/Hz \\ \hline Energy efficiency & High & Ultra-high \\ \hline Connection density & 106 devices/km\({}^{2}\) & 107 devices/km\({}^{2}\) \\ \hline Coverage & 99.99\% & 99.9999\% \\ \hline Positioning precision & Meter precision & Centimeter precision \\ \hline Satellite integration & Partial & Fully \\ \hline Automation integration & Partial & Fully \\ \hline Network awareness & Partial intelligibility & Ubiquitous intelligence \\ \hline Reliability & 1–10\({}^{-5}\) & 1–10\({}^{-9}\) \\ \hline Service level & VR/AR/3D & Tactile \\ \hline XR & Partial & Fully \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of 5G and 6G communication techniques [28; 40; 48; 49; 50; 51; 52; 53; 54]. Figure 2: Key capabilities of 6G networks [48].
# Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space +
## 4 Model  
The proposed system maps between the equaliser parameter space, consisting of 13 filter parameters and a two-dimensional plane, while preserving the context-dependent nature of the audio effect. After an initial training phase, the user can then submit (\(x,y\)) coordinates to the system using a track-pad interface, resulting in a timbral modification via the corresponding filter parameters. To demonstrate this, we train the model with two class (_bright_, _warm_) musical semantics data taken from the SAFE equaliser database, thus resulting in an underlying transition between opposing timbral descriptors in two-dimensional space. By training the model in this manner, we intend to retain the high-dimensional structure of the dataset in the two-dimensional space while minimising the reconstruction error inherent to dimensionality reduction methods.  
The model (illustrated in Figure 2) has two key operations. The first involves weighting the parameters by computing the vector \(\alpha_{n}(A)\) from the input signal long-term spectral energy (\(A\)). We can then modify the parameter vector (\(P\)) to obtain a weighted vector (\(P^{\prime}\)). The second component scales the dimensionality of (\(P^{\prime}\)), resulting in a compact audio-dependent representation. During the model implementation phase, we apply an unweighting procedure based on the (\(x,\,y\)) coordinates and the signal modified spectrum. This is done by multiplying the estimated parameters with the inverse weight vector, resulting in an approximation of the original parameters. In addition to the weighting and dimensionality reduction stages, a scale-normalisation procedure is applied, aiming to convert the ranges of each parameter (given in Table 1), to (\(0<p_{n}<1\)). This converts the data into a suitable format for dimensionality reduction.
## 5 Result Discussion
### Comparison of Acoustic Emission Signal and Deflection Value  
Based on the experimental data presented in Section 3, the variation of mid-span deflection with load for P1 and P3 beams is shown in Figure 12.  
Figure 12a illustrates that the load-deflection curve for the P1 beam exhibits a roughly constant slope without significant inflection points prior to reaching 40 kN. The P1 beam undergoes the stages of stable crack propagation and through-crack propagation before reaching 40 kN, as evidenced by the characteristics of the acoustic emission (AE) signals. At the transition between these two stages, there are significant increases in AE signals. When the load is further increased to 50 kN, the P1 beam enters the stage of ultimate failure, which is reflected in an increased slope in the load-deflection curve. At this stage, the AE signal characteristics also show significant increases.  
Figure 12b reveals that during the loading of the P3 beam from 0 to 10 kN, there is minimal change in deflection. As analyzed earlier, this stage corresponds to the balance between the external load and internal prestress. It leads to nearly zero stress at the mid-span section and minimal acoustic emission (AE) signals. When the load is in the range of 10 to 65 kN, two processes occur: the tensioning of the bottom concrete at the mid-span section until cracking and the combined action of the tensioned steel reinforcement and concrete after cracking occurs. Finally, when the load reaches 80 to 85 kN, there is a sudden increase in deflection. This phenomenon occurs as the beam approaches its ultimate load, where cracks and microdamage within the beam gradually accumulate and expand, leading to a sharp reduction in its rigidity and strength. Consequently, the beam's deflection shows a significant increase at this stage due to its load-bearing capacity nearing its limit and being unable to withstand further loads, resulting in more significant deformations. At this point, the top concrete at the mid-span section of the test beam is crushed, indicating the complete  
Figure 12: Mid-span load–deflection curves of P1 and P3 beams. **(a)** P1: load–deflection curve; **(b)** P3: load–deflection curve.  
failure of the beam. These observations lead to the conclusion that the four stages of loading and failure in prestressed concrete beams, as identified by the characteristics of AE signals, are not strictly separated but rather intertwined and overlapping. However, both the AE and deflection results show a notable deviation from the original trend as the load approaches 70 kN. This aligns with the occurrence of high-frequency, high-amplitude AE signals and the clear visibility of mid-span cracks, indicating that 70 kN can be considered a turning point in crack development.
## 3 The Time Source Techniques
### Pulsar Star  
A pulsar is a rotating neutron star with strong magnetic fields that emit beams of electromagnetic radiation from its magnetic poles, which can be detected when these beams are directed toward Earth, as shown in Figure 2. The electromagnetic radiation from these rotating neutron stars spans the entire spectrum, making them remarkable celestial objects detectable across every observational window of the cosmos [57]. Sincethe discovery of the first pulsar in 1967, the number of known pulsars has rapidly grown. Today, over 3000 neutron stars have been detected across wavelengths ranging from radio to high-energy bands [58].
## 4 Implementation and Application Scenarios
### Application Scenario for Real-Time Information Search Context-Aware Service  
The diagram representing the application scenario for automated context-aware service according to the following sequence is demonstrated in Figure 19.  
Figure 19: Sequence Diagram for Real-time Information Search Context-Aware Service.  
1. Agricultural data are acquired by the WSAN deployed in a maize field of the \"Kopaida\" farm (as in Table 5) and transmitted to the gateway.
2. The gateway forwards the raw data to the context-aware middleware cloud into the Microsoft Azure platform.
3. The raw data are processed and classified as high-level contextual data into the context management module of the middleware cloud.
4. The contextual data are sent to the cloud server running in the Azure platform and stored in a data source as context history.
5. The farmer accesses the system via a mobile application supported by conversational user interface and requests information concerning the daily cultivation soil moisture use for a specific date. The request is forwarded in real time to the context-aware middleware cloud.
6. The context-aware middleware cloud sends a request to the cloud server for accessing the related data stored in a data source.
7. The cloud server responds by returning to the context-aware middleware cloud the requested contextual data concerning the request.
8. The context-aware middleware cloud processes the contextual data and responds to the farmer via the mobile application by sending all the requested information.
# Review of Unmanned Aerial Vehicle Swarm Communication Architectures and Routing Protocols
## 3 Routing Protocols
### The Classification of Routing Protocols  
At the beginning of the research and experiment of UAV swarm communication, researchers studied and optimized the routing protocols of traditional Ad hoc networks. However, as the study progressed, the researchers found that most of the traditional routing protocols were not suitable for UAV swarm communication. On the one hand, UAV nodes have properties different from traditional nodes, such as the rapid mobility. On the other hand, different task scenarios have different requirements for routing protocols. Therefore, to get a routing protocol for a swarm of UAVs in Ad hoc networks, the researchers improved some traditional routing protocols. At the same time, a number of new, dedicated routing protocols for UAV swarm have been proposed. As shown in Figure 7, the protocols can be divided into three large categories and many subclasses.  
In the following sections, we detail these routing protocols according to the categories shown in Figure 7. For the highlights, we also clearly state the original works and the subsequent follow-up studies. A discussion of the advantages and disadvantages of various routing protocols and usage scenarios follows.
## 5 Discussion  
The results for the three cases show an improvement when the features are fused compared to when each sensor is analyzed independently, complementing the measurements of each kind of signal and making the system more robust and capable of correctly classifying the conditions. In all cases, the bearing with no damage (HLT) is well separated from the rest of the conditions. This could indicate a significant influence of the contamination, as  
Figure 12: Analysis results for the fused vibration–current data: (**a**) 3D projection of the fused data after the LDA process and the hyperplanes created by the SVM model; (**b**) confusion matrix for validating the SVM model, achieving 97.1% accuracy.  
the faulty condition clusters are closer together. For the different OR defects, the projection of the features from the current signals presents considerable overlapping (Figure 10a), and the SVM model was not capable of classifying the conditions, as shown in Figure 10b; on the other hand, vibration data (Figure 11a) separate the OR damages better than when using current data, but the classification carried out with the SVM presents problems in correctly identifying the 3 mm and 5 mm holes, while recognizing the 1 mm hole better. As mentioned, the fusion data allow better distinction between the 3 and 5 mm defects in the OR, as seen in Figure 12b. This research contributes to the detection of common conditions that affect bearings, OR defects, and lubricant contamination, presenting the possibility of discriminating between the defects and their combinations by considering different severities of OR damage.  
Additionally, Table 3 compares the proposed model to other approaches previously exposed in the literature, presenting an accuracy level similar to those works. It is possible to appreciate the primary use of vibration signals and neural networks, as well as the focus on defects located on the parts of the bearing, and a lack of studies on lubricant contamination, even though it is a leading cause of different faults [4], and the fusion of different sources of information. This comparison allows the identification of the possibilities and less explored areas in bearing condition monitoring to focus future works on new techniques and different faulty conditions.
# Initial Assessment of the LEO Based Navigation Signal Augmentation System from Luojia-1A Satellite
## 4 In-Orbit Performance Evaluation Results
### In-Orbit Evaluation of the Orbit and Clock
During the augmentation signal transmitting, the onboard OCXO is activated. The clock bias is determined from the onboard orbit determination and time synchronization method and then broadcasted to the users. The onboard GNSS receivers and the ranging signal transmitter is driven by the same OCXO, which ensures the precise time synchronization between the onboard receiver and transmitter. The latency caused by the onboard circuit is affected by the temperature. The temperature of the payload is controlled by the active temperature control system of the satellite platform. In this study, the hardware delay is modeled as a constant during the augmentation experiment. Considering the complexity of the space environment and the satellite motion status, the in-orbit oscillator behavior needs to be evaluated. Since the OCXO presents significant frequency drift, the stability of the clock is assessed with the Hardamard variance [49; 50]. Given the phase data \(\{x_{i}\},i=1,2,\cdots,\mathrm{N}\) with sampling interval \(\tau_{0}\), then the Hardamard variance can be calculated with:  
\[\sigma_{H}^{2}(\tau)=\frac{1}{6\tau^{2}(\mathrm{N}-3)}\sum_{i=1}^{N-3}[x_{i+3} -3x_{i+2}+3x_{i+1}-x_{i}]^{2} \tag{5}\]  
where \(x_{i}\) is the \(i\)th phase values at the averaging time \(\tau\). The Luojia-1A satellite carries a small, low-cost OCXO as its frequency standard, whose nominal frequency stability is \(1\times 10^{-10}\). The clock assessment results are presented in the left panel of Figure 7. The figure indicates that the clock equipped on Luojia-1A presents different characteristics with the navigation satellite clocks. The in-orbit stability of the oscillator reaches \(3\times 10^{-10}\), which is achieved at \(\tau=70\) s. The frequency drift results in a quadratic form in the clock bias. Fortunately, the frequency drift does not affect the short-term clock stability and the signals tracking. The right panel of Figure 7 presents the frequency drift observed by the onboard oscillator. The time series is rather noisy, with a non-zero mean. The average frequency drift for the oscillator is \(2.68\times 10^{-10}\). Since the relativistic effect on the oscillator is not compensated in Luojia-1A, so the frequency drift is likely to be caused by the relativistic effect. The impact of the relativistic effect (sum of general and special relativistic) for Luojia-1A frequencies is around \(2.5\times 10^{-10}\), so whether there is remaining frequency drift need to be further verified. The figure indicates that the time series is fairly stable, so the frequency drift can be treated as a constant, which can be calibrated in the data processing stage. Figure 6: Internal precision of the Luojia-1A broadcast ephemeris.
###### Abstract
In the evaluation of anchored slope stability using analytical methods, many researchers have focused on improving limit equilibrium analysis for assessing the stability of rock slopes. [PERSON] [15] and [PERSON] [6] applied an enhanced limit equilibrium method to evaluate the stability of anchored slopes. [PERSON] [16] and [PERSON] [17] combined limit analysis and the small potential energy principle with the pseudo-dynamic method, proposing a stability evaluation approach for slopes under seismic conditions that considers the dynamic variation of anchor axial forces. In the evaluation of anchored slope stability based on monitoring and detection analysis, [PERSON] [18] discussed the comprehensive stability assessment of anchored slopes based on residual tension changes and proposed a method for evaluating slope stability through pull-out testing. [PERSON] [19] and [PERSON] [20] conducted continuous monitoring of slope deformation following reinforcement and analyzed the monitoring data to assess the stability of an anchored layered rock slope example. [PERSON] [21] developed a remote monitoring and prediction system for landslides using constant resistance to large Deformation (CRLD) anchors, which was used for stability forecasting of rock slope-anchoring systems. [PERSON] [14] analyzed the stability of anchored slopes by monitoring changes in structural forces in anchor rods and cables during excavation and after the completion of excavation. In the study of rock slope anchoring system stability through model testing [22], [PERSON] [23] conducted indoor physical model experiments on collapse control and monitoring, investigating the collapse failure characteristics and overall control effectiveness of energy-absorbing reinforcement measures. Based on the above, a summary of existing stability evaluation methods for rock slope anchorage systems is provided in Table 1 below. However, these methods still have certain limitations. The rigid body limit equilibrium method is based on several assumptions, which result in discrepancies between the calculated conditions and actual engineering situations, leading to evaluation bias [22; 24; 25]. Furthermore, the limit equilibrium analysis method only treats the anchoring force as an equivalent external force, without considering the interaction between the anchoring structure and the rock slope, which impacts the accuracy of the stability assessment for the entire rock slope anchoring system. Finite element analysis encounters challenges in modeling rock fractures and discontinuities. These fractures significantly affect slope stability; however, traditional finite element methods struggle to model discontinuous elements, such as the formation and propagation of fractures [26; 27]. In conventional discrete element method (DEM)-based slope stability evaluations, model parameters are predominantly derived from laboratory tests. However, rock masses exhibit significant discontinuity, anisotropy, and heterogeneity [27], which lead to considerable uncertainty in their mechanical properties [28]. Moreover, external factors such as rainfall [29], weather
# An ANN-Based Approach for Real-Time Scheduling in Cloud Manufacturing
## 4 Numerical Results
### Performance with Discussions  
We use the modified NSGA-II (Nondominated Sorting Genetic Algorithm version II) [35] as the referred method for the comparative experiment. Specifically, the corresponding modification is based on the approximation that the whole problem is divided into the time dimension initially, then NSGA-II(\(\alpha\)) is called to solve the divided sub-problems one by one. The problem segmentation factor \(\alpha\in(0,1]\) indicates the degree of the division, that \(\alpha=1\) means no division and \(\alpha\to 0\) will mimic the effect of real-time scheduling. Figure 11a-e show the performance comparison of these methods in terms of total cost, service satisfaction, and make-span. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **Precision** & **Recall** & **F1-Score** \\ \hline Completion status 0 & 0.848 & 0.943 & 0.893 \\ Completion status 1 & 0.848 & 0.700 & 0.767 \\ Completion status 2 & 0.968 & 0.972 & 0.970 \\ \hline Macro average & 0.888 & 0.872 & 0.877 \\ Weighted average & 0.944 & 0.944 & 0.943 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Conventional evaluation metrics for the ANN. Figure 10: Prediction accuracy of the ANN model over 1000 training epochs. Figure 11: Performance comparison on various MT Scales, (\(\alpha\)) represents NSGA-II(\(\alpha\)) in the x-labels, \(\alpha\in\{0.05,0.25,0.50,1.00\}\). Since the dataset groups for these experiments mentioned in Section 4.1 are dimensionless quantities, we use comparison ration in Equation (40) to evaluate the performance of the scheduling methods. \[\rho_{obj}=\left\{ \begin{array}{ll}\frac{\overline{Z_{obj}}\left( \text{ANN}\right)}{\max\limits_{\alpha}\left\{\overline{Z_{obj}}\left(\text{ NSGA-II}(\alpha)\right)\right\}}&\text{if }obj=u\\ \frac{\min\limits_{\alpha}\left\{\overline{Z_{obj}}\left(\text{ NSGA-II}(\alpha)\right)\right\}}{\overline{Z_{obj}}\left(\text{ANN}\right)}&\text{ otherwise}\end{array} \right.,\quad obj\in\left\{m,u,d\right\} \tag{40}\]  
where \(\overline{Z_{obj}}(\text{method})\) means the average of the objective value gained by the corresponding method, and the summarized experimental results are listed in Table 6.  
In each group of the dataset, the ANN-based method has an absolute advantage in the cost (\(Z_{d}\)) compared to the NSGA-II(\(\alpha\)) method series. Although the advantages of ANN in make-span (\(Z_{d}\)) and service satisfaction (\(Z_{u}\)) are not significant, this method has also reached higher levels in both criteria. For the NSGA-II(\(\alpha\)) method series, as the value of division factor \(\alpha\) increases, the number of sub-problems decreases, which leads to the decrease of cost, but the make-span increases. As for the service satisfaction, all of the methods perform similarly, which are mainly due to the limited number of candidate MRs in each MS. It can be inferred that the ANN-based approach has a strong generalization ability since it has a great performance in different dataset groups.