esa-sceva/satcom-chunk-collection huggingface dataset:
# DeNERT-KG: Named Entity and Relation Extraction Model Using DQN, Knowledge Graph, and BERT
## 4 Experiments
### Results
#### 4.3.2 Result for Relation Extraction Task  
DeNERT-KG, which we propose, extracts subject and object through NER, and finally uses a knowledge graph to extract the relationship between subject and object. To verify the model of DeNERT-KG from which the relationship was finally extracted, we utilized the TACRED dataset containing the relationship information in the experiment.  
First, an experiment was conducted to verify the performance improvement when using knowledge graph embedding to extract relationship information. Table 6 is the result of conducting experiments on various variants of BERT and BERT and adding knowledge graph embedding. We are proposing ways to apply knowledge graph embedding to extract relationship information. As shown in Table 6, it can be seen that better performance was achieved when applying knowledge graph embedding was applied, in comparison to simply applying a language model. Finally, we were able to get the best results when we used the DeNERT-KG model with ALBERT and knowledge graph embedding in DeNERT.  
Table 7 is a comparative test of the same TACRED dataset with other existing studies. The DeNERT-KG we propose shows the highest F1-score, as we can see in the table. Although the proposed model did not achieve higher performance improvements than the existing one, the existing models  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** \\ \hline BERT-only & 66.4 & 66.8 & 66.6 \\ BERT + KG & 72.6 & 68.3 & 70.4 \\ Electra-base + KG & 48.2 & 43.6 & 45.8 \\
**ALBERT + KG** & **71.8** & **73.1** & **72.4** \\
**(DeNERT-KG)** & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experiment result on TACRED dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1** \\ \hline BERT-Base & 77.52 & 70.58 & 73.89 \\ BERT-Base + POS & 78.82 & 70.84 & 74.62 \\ BERT-Large & 80.25 & 73.89 & 76.94 \\
**BERT-Large + POS (DeNERT)** & 81.97 & 74.13 & **77.85** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experiment result on EWNERTC dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Model** & **F1-Score** \\ \hline CoNLL 2003 & [PERSON] et al., 2019 [38] & 93.18 \\ [PERSON] et al., 2020 [39] & 91.47 \\ [PERSON] et al., 2017 [40] & 91.93 \\
**DeNERT** & **93.45** \\ \hline \multirow{4}{*}{W-NUT 2017} & [PERSON] et al., 2017 [41] & 41.06 \\  & [PERSON] et al., 2019 [37] & 41.86 \\ \cline{1-1}  & [PERSON] et al., 2019 [38] & **49.59** \\ \cline{1-1}  & [PERSON] et al., 2020 [39] & 40.59 \\ \cline{1-1}  & **DeNERT** & 45.78 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison result on CoNLL 2003 and W-NUT 2017 dataset.  
extract only relevant information from the sentences. We can see the meaningful result because we can extract the subject and the object through the DeNERT model, extract the relationship between the two, and finally identify up to {subject, subject_type, object, object_type, and relation}.
## 5 Conclusions  
A grain size analysis was conducted using 143 modern flood sediment samples from six sections of the lower Yellow River in Jinan City, Shandong Province, and calculations of grain size parameters using the GM and MM were compared. The primary conclusions drawn from this study are as follows:  
1. The modern flood sediments observed in the lower Yellow River primarily consisted of fine particles with a mean grain size of 3.52-6.26 \(\Phi\) (as computed by the MM), denoting silty sand, and sorting coefficients that ranged from 1.22 to 1.81, indicating poor sorting. The skewness values ranged from 0.91 to 2.07, with an average of 1.39, indicating extremely positive skewness. Meanwhile, the kurtosis values varied from 0.84 to 2.06, with an average of 1.33, suggesting sharp peaks.
2. The mean grain sizes calculated using both the GM and MM exhibited a strong correlation; therefore, both methods can be used to distinguish the grain size characteristics of flood sediment in the lower Yellow River. Furthermore, the sorting coefficient between the two methods was strongly correlated, reinforcing their consistency when providing qualitative sample descriptions.
3. The skewness and kurtosis parameters computed using the GM and MM exhibited moderate correlations and no correlations, respectively. Considering this, from a theoretical standpoint, it is recommended to utilize the MM to analyze the skewness of flood sediments in the lower Yellow River, as it would more accurately reflect the overall sediment grain size information. Moreover, the GM should be employed to analyze the kurtosis parameters, as this method emphasizes the principal dynamic characteristics of flood sediment deposition. The above understanding also has important reference significance for the grain size analysis of fine-grained flood sediments in other rivers of the world.  
Conceptualization, L.T.; methodology, H.W.; software, W.Z.; validation, S.Z. and W.Z.; formal analysis, L.T., S.Z. and W.Z.; investigation, L.T. and S.Z.; resources, H.W.; data curation, S.Z.; writing--original draft preparation, L.T.; writing--review and editing, L.T., S.Z., H.W. and W.Z; visualization, L.T. and W.Z.; supervision, H.W.; project administration, L.T. and S.Z.; funding acquisition, L.T. and S.Z. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The data that support the findings of this study are available on request from the corresponding author ([PERSON]).  
We are grateful to [PERSON], [PERSON] and [PERSON] for their help with the field sampling. We also thank [PERSON] (The University of Hull, UK) for English language editing. Two anonymous reviewers are thanked for their critical comments on the original manuscript.  
The authors declare no conflict of interest.
## 4 Bespoke Space System Design  
The discussion in Section 3 identifies that the ideal requirements for routine archaeological detection and monitoring in Scotland, as defined by the requirements outlined in Section 2, cannot be achieved by using satellite data acquired for general purposes, which is not to negate the potential utility of such data in other contexts (e.g., [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]). Tasking a constellation of 12 spacecraft did allow for suitable imagery to be collected of a small region of Scotland, but the competing needs of other customers  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**System** & **Operator** & **Multi-Spectral** & **Panchromatic** & **Approximate** \\  & & **GSD** & **GSD** & **Revisit Time** \\ \hline Worldview-2 & MAXAR/DigitalGlobe & 1.84 m & 0.46 m & 1–4 days \\ \hline Worldview-3 & MAXAR/DigitalGlobe & 1.24 m & 0.31 m & 1–4 days \\ \hline GeoEye-1 & MAXAR/DigitalGlobe & 1.84 m & 0.46 m & 3 days \\ \hline SkySat & Planet & 1 m & 0.5 m & \textless{}1 day \\ \hline Pleiades-1A + 1B & AIRBUS Defence and Space & 2 m & 0.5 m & 1 day \\ \hline DMC3-TripleSat-1 & DMCii & 3.2 m & 0.8 m & 1 day \\ \hline DMC3-TripleSat-3 & DMCii & 3.2 m & 0.8 m & 1 day \\ \hline SuperView-1 & Beijing Space View Technology & 2 m & 0.5 m & 2 days \\ \hline KOMPSAT-3 & Korea Aerospace Research Institute & 2.2 m & 0.5 m & 1.4 days \\ \hline Gaofen-2 & China National Space Administration & 3.2 m & 0.8 m & 5 days \\ \hline Deimos-2 & Urthecast & 3 m & 0.75 m & 2 days \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selection of existing very-high-resolution multi-spectral satellite imaging systems [21; 22; 23; 24].  
Figure 3: Stranraer area of interest [26]. Centred on 54\({}^{\circ}\) 52′27′N, 4\({}^{\circ}\) 58′ 76′′W; see Figure 2 for location. © Planet Labs Inc. 2020.  
imply that extending this to provide routine, systematic coverage of the entirety of Scotland would be challenging. This reinforces many of the limitations to the effectiveness of archaeological applications of satellite data discussed in general terms in Section 1. From that position, a high-level design for a bespoke space system is developed and presented. It should be noted that this design is based on requirements derived for specific archaeological priorities in Scotland. As such, the space system specified may not be ideal for archaeological purposes elsewhere; however, the design methodology presented, and the general insights obtained, could be applied to any region.
## 2 Related Work
utilized wavelet decomposition to smooth the data, which is beneficial for the model to extract long-term relational features [33]. Compared with wavelet decomposition and EMD, VMD can better solve the problems of modal mixing and frequency adaptation, which is beneficial for the model prediction accuracy. Several studies have demonstrated that VMD has a more significant enhancement effect on prediction compared to other methods [34; 35; 36; 37]. [PERSON] et al. [38] employed VMD to decompose traffic flow data into different modal components, extracted features from each modal component using an LSTM model, and achieved multi-step prediction by accumulating the predicted values of previous steps. [PERSON] et al. [39] converted traffic flow data into different frequency components using VMD, optimized weights using ICA algorithm, and integrated three prediction models (GMDH, BILSTM, and ELMAN) to predict different modal components. [PERSON] et al. [40] decomposed environmental variables using VMD and found the most power-relevant modal components based on MIC and Pearson correlation coefficients, predicting them along with historical power data. [PERSON] et al. [41] utilized GRU models to achieve prediction of low frequency components and TCN models to achieve prediction of high frequency components after VMD modal decomposition. Finally, they reconstructed the prediction results of low and high frequency data as final prediction values. Overall, hybrid models formed by combining data decomposition methods and prediction models generally achieve good prediction results in traffic flow prediction.
###### Abstract  
The mobility of low Earth orbit (LEO) satellites causes the LEO satellite network to experience topology changes. Topology change includes periodic topology change that occurs naturally and unpredictable topology change that occurs due to instability of the inter-satellite link between satellites. Periodic and unpredictable topology change causes frequent topology change, requiring massive communications throughout the network due to frequent route convergence. LEO satellites have limited onboard power because they operate on batteries. The waste of limited satellite onboard resources shortens the lifespan of the satellite, and achieving stable end-to-end transmission is challenging for the network. In this regard, minimizing communication overhead is a fundamental consideration when designing a routing scheme. In this paper, we propose a distributed detour routing scheme with minimal communication overhead. This routing scheme consists of a rapid detour, selective flooding, and link recovery procedures. When a link failure occurs in the network, a rapid detour can detect link failure using only a precalculated routing table. Subsequently, selective flooding searches for the optimal detour point within the minimum hop region and flood to detour point. After link recovery, a procedure is defined to traverse the pre-detour path and switch it back to the original path. The simulation results show that the proposed routing scheme achieves a reduction of communication overhead by 97.6% compared with the \(n\)-hop flooding approach.  
LEO satellite; distributed routing; flooding; communication overhead; link failure
# Technological Advancement in Tower-Based Canopy Reflectance Monitoring: The AMSPEC-III System
## 2 Materials and Methods
### Data Processing
We focus the results on the retrieval of the Photochemical Reflectance Index (PRI; [23]), as well as demonstrate the directional effects using three spectral channels: green (559 nm), red (660.3 nm) and NIR (809.7 nm).
###### Abstract
Unlike CHM-based methods, the point-cloud-based method applies cluster algorithms (e.g., Kmeans clusters) based on point clouds for searching 3D clusters as individual tree segmentation [19; 26; 40]. Theoretically, point-cloud-based methods have higher accuracy for ITDS, but require more computation time [41] comparing to CHM. Most of the point-cloud-based methods require algorithms for seed point selections [42; 43]. Trunk points located at the base of trees were often selected as seed points for bottom-up ITS, which has been proven to be more accurate than up-bottom segmentation especially in broadleaved forests [28; 44; 45]. However, due to the high canopy cover in the growing season of deciduous broadleaved forests or evergreen broadleaved forests, it is difficult to extract a seed point from the bottom of the tree in this application. Various cluster algorithms have been tested for ITS, including region growing [42], Kmeans [46], mean shift [47; 48], normalized cut [19], density-based spatial clustering of applications with noise (DBSCAN) [49], and deep learning network [43]. Although these clustering algorithms are effective in forests, their effectiveness relies heavily on the accuracy of ITD and thus remains a challenge in broadleaved forests with overlapping tree canopies and similar height and density distributions [7]. Thus, a gap remains for an efficient, accurate, and applicable ITDS method for broadleaved forests. Previous studies have improved the accuracy for extracting forest parameters [16] and classifying tree species [50; 51] by merging LiDAR point clouds from leaf-off and leaf-on seasons to extract forest variables or classify forest species. However, few studies have focused on ITDS using multi-temporal UAV-LiDAR data. Since trunk points contribute to improving the accuracy of ITD and bottom-up segmentation methods [28; 44; 45], it is worthwhile to explore the potential for combining multi-temporal ITDS point clouds in broadleaved forests; point clouds from leaf-off season could capture dense tree trunk points and point clouds from leaf-on season would obtain detailed 3D information of tree canopies. To address the challenges mentioned by the utilization of UAV-LiDAR data in broadleaved forests and to develop a more efficient, precise, and practical methodology, this study proposed a new ITDS framework based on point clouds from multiple seasons in broadleaved forests. Our specific objectives are as follows: (1) to develop a strategy for automatic ITD from UAV-LiDAR point clouds in leaf-off season; (2) to explore the best ITS strategy based on the seed points from the automatic ITD and UAV-LiDAR point clouds in leaf-on season; and (3) to improve the efficiency and capacity of the developed ITDS framework through a unique refining process.
# Threat Detection Model for WLAN of Simulated Data Using Deep Convolutional Neural Network
## 2 Materials and Methods
### System Parameters Setting  
1. For each router, we generated a 5000 Non-HT dataset for WLAN beacon frames.
2. For the known routers, we used the router MAC addresses as known labels, while the remaining are \"Unknown\" labels.
3. The developed CNN is trained to detect any unknown routers and to categorize the known routers.
4. We divide the data into a test of 10%, validation also of 10%, and training of the remaining 80%.
5. The signal-to-noise ratio (SNR) is considered to work on a 5 GHz band and 20 dB.
6. The numbers of known and unknown devices are flexible. In this paper, the simulation applies two scenarios: 4 known against 10 unknown devices first and 7 known against 3 unknown devices the second time, with the ability to increase or decrease these numbers.
7. The number of routers that are marked as \"unknown\" is assumed to be greater and less than that for the known devices to model the variability in the dataset for the RF signatures of the unknown router. Table 1 shows the set values of the model parameters.  
Figure 2: A user is linked to a mobile hot spot and a router, where the observer decodes the MAC address and collects beacon frames after training.  
For the considered dataset, the network training taking longer than 3 min with an Intel(r) Xeon(r) @ 3.6 GHz W-2132 CPU and about 34 s NVIDIA(r) 3080 GPU GeForce RTX. The production of 5000 frames consumes about 153 min on a computer of W-213 CPU @ 3.66 GHz Intel(r) Xeon(r) with 32 GB memory.
## 3 On Orbit Operations and Anomaly Analysis
### Critical Anomaly-Loss of Communication
The CMOS Schmitt trigger inverter IC (G1) was the control element used to realize the hardware restart, and was connected to the solid-state relay serving as the main power switch (Q4), connecting the spacecraft avionics load (VBAT) to the battery (VBAT_Reset). The CMOS input was connected to the output of a solid-state relay (Q2), which was normally held in the low voltage (off) state. Under such normal operating conditions, the low voltage at the CMOS input would hold the enable pin (EN) of the main switch (Q4) at a high voltage, thereby ensuring that the main power switch was turned on. If CDH were to send the reset signal, the CMOS input terminal would turn to a high voltage, resulting in a low voltage output to the main power switch, causing the spacecraft avionics to be disconnected from the battery. The capacitor (C42) would initially be charged, and would hold the main power switch in an off state until it discharged sufficiently through resistor R74 to return the potential to the original level and return the main power switch to the on state. Figure 26: F10.7 solar activity index (top) and Kp geomagnetic disturbance index (bottom) from launch to 23 February. CMOS ICs are at risk of single event latch-up (SEL) when exposed to ionizing radiation [38; 39], causing the component to lose functionality, while also exhibiting a low impedance state between power and ground. If an SEL were to occur in G1, the EN of Q4 would also be affected, causing a spacecraft power down until the latch-up could be cleared. An SEL can be cleared by power cycling the component, or will persist until the power supply cannot provide the source voltage and current required. Since G1 was directly connected to the battery following spacecraft deployment (kill switch Kill_SW-RTN would be permanently enabled), the only way of clearing an SEL would be for the battery to discharge sufficiently, which is consistent with the 1.5 month blackout, as well as the signs of battery discharge following power on. The high currents and joule heating resulting from an SEL could cause irreparable degradation and damage to the component if not cleared quickly, and would result in reduced component lifetime. The need to ensure that all key circuits are latch-up-proof is an important lesson learned. Revisions have been made to the EPS design to implement overcurrent protection across the inverter power line to ensure that any latch-up will result in the inverter power supply being cut. A two-step reset concept is also being implemented, wherein the inverter is normally in a powered down state, being powered up only in the event a reset is commanded. The inverter is not at risk of latch-up when in a powered down state. Radiation tests using proton bombardment will also be performed for the revised CDH/EPS stack to verify the new design, as well as longer duration stress tests.
# Survivable Deployments of Optical Sensor Networks against Multiple Failures and Disasters: A Survey
## 2 Existing Survivability Technologies in OSNs
### The Performance Comparisons
Since the pre-configured \(K\&K\) structure scheme and the multi-path protection scheme are developed to resist multiple failures, the type of applicable topology was mesh. The \(1+1\) protection scheme had a wide application scope including mesh, ring, star, and line. The p-cycle scheme was designed for mesh networks.
###### Abstract
FLFR learn from previous faults and solutions, improving their effectiveness over time. This continuous learning approach helps to prevent the recurrence of similar faults in the future. The method applies a set of test cases to execute the program under test. Then, we can obtain the program spectrum information after execution and convert the program spectrum information into coverage matrices and result vectors. Next, we analyze the 13 typical spectrum-based methods and obtain the ranking of the suspected faults of the statements contained in the program under test. These rankings will be used to improve the fitness function construction of the genetic algorithm (GA). We use an improved genetic algorithm to generate new test cases. The process is iterative. At last, we get the top-ranked statements that actually contain faults after multiple iterations. The contribution of the paper is three-fold, as summarized in the following:  
* We propose FLFR, which improves the genetic algorithm. Specifically, we add the localization parameter of the suspected faulty sentence to the genetic algorithm's fitness function. This way, when generating new test cases, we can improve the coverage of these suspected fault statements. In addition, we have adaptively designed the genetic algorithm's crossover probability and mutation operator. On the one hand, the diversity of the population is considered, and, on the other hand, the overall search efficiency is improved. * We use FLFR in 13 typical fault spectrum calculation methods. We analyzed the effects of the FLFR method from the perspective of different failure types and different iterations. * We conduct experiments on six datasets including C and Java. In addition, we compare 13 typical software fault localization approaches. The experiments show that the test cases supplemented by FLFR can improve the efficiency of software fault localization. The rest of this paper is organized as follows. Section 1 presents the research motivation. The approach framework and improved GA algorithm are described in Section 2. Section 3 presents the experiments for evaluating FLFR, the results of which are reported in Section 4. Section 5 presents the related work. Finally, we conclude this paper in Section 6.
# Spatio-Temporal Changes in Water Use Efficiency and Its Driving Factors in Central Asia (2001-2021)
## 3 Results
### Analysis of the Impact of Driving Factors  
#### 3.3.1 Detection Factor Influence  
The factor detection results of the impact of each driver on WUE are shown in Figure 5. All drivers passed the significance test (\(p=0.001\)) from 2001 to 2021. The factor analysis shows that LST has a dominant effect on WUE (q = 0.548), and GWS has the least driving effect on WUE (q = 0.012). LST, DEF, VPD, RN, TEM, PRE, and VSW1 have a high degree of influence on WUE, explaining more than 45% of the variation in WUE. VAP, AOD, PDSI, and GWS have a low degree of influence on WUE, explaining less than 20% of the variation.  
Figure 4: Time series variation of annual average WUE in Central Asia, 2001–2021 ((**A**), temporal information entropy; (**B**), time series information entropy).  
Figure 5: Factor q values for the impact of each driver on WUE.