esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Open Discussion and Future Outlook  
In this work, we attempted to investigate the resource conflict problem in the practical application scenario of QKDON and present a model to solve the problem. Our results validate the feasibility of the model. Nevertheless, some assumptions are made in this model, such as the initial number of wavelengths and keys and the service traffic. We aim to consider the impact of QKD on the network comprehensively. We discuss several issues which will be explored in future work, below.  
1. **Allocation of multiple network resources:** The routing scheme proposed in this paper only considers the allocation of the wavelength and key resources. Based on the use of WDM technology to construct QKDON, TDM and SDM technologies further improve resource utilization and transmission capacity. Therefore, the introduction of the above two techniques and the allocation of time slots and fiber resources can be considered in future research.
2. **Survivability of QKDON:** With the development of QKD technology, the survivability issues of QKDON have also received extensive attention. Actually, due to factors such as natural disasters or man-made damage, link failure will inevitably occur. The study of the survivability of QKDON is of great importance for the practicality of QKD. Survivability involves two aspects: protection and recovery. Different from classical optical networks, the failure of QKDON will make it impossible to generate quantum keys. Therefore, how to protect the quantum channel in the network and recover the service transmission in time after the failure is an important research topic.
3. **Applicability of QKD and other encryption methods:** Post-quantum cryptography (PQC) is another secure encryption method that can be used to resist quantum computing in addition to QKD. QKD solves the key distribution problem, while PQC studies the security of cryptographic algorithms in quantum environments. QKD and PQC differ in terms of practical conditions and application scenarios. Assessing the applicability of QKD and PQC for different security requirements is a worthwhile research topic in the network. In addition, we need to compare the costs of deploying QKD devices and PQC in different security-demanding circumstances.
## 5 Experimental Results and Discussion
### Real-World Application: Circulating Fluidized Bed Boiler
This improvement can be attributed to the proposed method's ability to rescale distance values using training data, much like attention mechanisms prioritize important features by weighting query vectors. Therefore, the proposed method can efficiently detect faults before the boiler shutdown by identifying anomalies early, which aids in establishing a maintenance plan. Table 3 compares the early fault detection times of the conventional and proposed methods for an unplanned CFBC boiler shutdown. The boiler was urgently shut down by the operator at 14:35. PCA and LOF detected the fault after the shutdown, with PCA showing the latest detection time among all models, highlighting its limitations in real-world applications. In contrast, kNN detected anomalies before the boiler shutdown. AAKR, AASKR-NP, and AASKR detected anomalies before the failure significantly impacted the system. Notably, the proposed method improved upon the traditional AAKR by rescaling the distance value based on the query vector. This enhancement allows the proposed method to detect early faults in real-world processes and address them before unplanned shutdowns occur. Moreover, the proposed method detected fault symptoms 13 min earlier  
Figure 12: Comparison of fault detection results of conventional and proposed methods: (**a–f**). than the experimental results of [PERSON] [41]. Consequently, the proposed method not only improves fault detection performance compared to conventional FDD methods and SNN but also enhances early fault detection capabilities. This improvement suggests its potential applicability across various industrial domains beyond thermal power plants.
# Modeling and Compensation of Motion Errors for 6-DOF Robotic Manipulators
## 5 Experimental Studies
### Compensation of Position Error under Different Loads
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Point** & \(\theta_{1}\) (°) & \(\theta_{2}\) (°) & \(\theta_{3}\) (°) & \(\theta_{4}\) (°) & \(\theta_{5}\) (°) & \(\theta_{6}\) (°) \\ \hline
1 & 0.05564 & \(-0.02996\) & \(-0.02568\) & 0 & \(-0.06509\) & \(0.06509\) \\
2 & \(-0.04750\) & \(0.02261\) & \(0.02489\) & 0 & \(1.5884\) & \(-0.15885\) \\
3 & 0.04343 & \(-0.03824\) & \(-0.00518\) & 0 & \(-0.011362\) & 0.11363 \\
4 & 0.02360 & \(-0.02690\) & \(0.00331\) & 0 & \(-0.04957\) & 0.04957 \\
5 & 0.05196 & \(-0.03822\) & \(-0.01373\) & 0 & \(-0.08925\) & 0.08926 \\
6 & 0.04691 & \(-0.03566\) & \(-0.01125\) & 0 & \(-0.08654\) & 0.08655 \\
7 & 0.05094 & \(-0.03143\) & \(-0.01951\) & 0 & \(-0.08495\) & 0.08495 \\
8 & 0.04230 & \(-0.02865\) & \(-0.01365\) & 0 & \(-0.07655\) & 0.07655 \\
9 & 0.03688 & \(-0.02649\) & \(-0.01038\) & 0 & \(-0.08305\) & 0.08306 \\
10 & \(-0.18010\) & 0.05644 & 0.12365 & 0 & 0.42957 & \(-0.42954\) \\
11 & 0.03615 & \(-0.02521\) & \(-0.01094\) & 0 & \(-0.05531\) & 0.05531 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Compensated joint angles \(\Delta q\) (under 10.6 N). based on joint stiffness and joint link stiffness is effective, which is significant for improving the spatial positioning accuracy of 6-DOF manipulator ends. Researchers should discuss the results and how they can be interpreted from the perspective of previous studies and of the working hypotheses. The findings and their implications should be discussed in the broadest context possible. Future research directions may also be highlighted. This section is not mandatory but can be added to the manuscript if the discussion is unusually long or complex. Figure 13: Position error before and after compensation under different loads. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Point** & \(\mathbf{\theta_{1}}\) (°) & \(\mathbf{\theta_{2}}\) (°) & \(\mathbf{\theta_{3}}\) (°) & \(\mathbf{\theta_{4}}\) (°) & \(\mathbf{\theta_{5}}\) (°) & \(\mathbf{\theta_{6}}\) (°) \\ \hline
1 & 0.00253 & \(-\)0.00316 & 0.00064 & 0 & \(-\)0.00534 & 0.00534 \\
2 & 0.00318 & \(-\)0.00293 & \(-\)0.00025 & 0 & \(-\)0.01629 & 0.01631 \\
3 & 0.00644 & \(-\)0.00624 & \(-\)0.00020 & 0 & \(-\)0.01777 & 0.01778 \\
4 & 0.00458 & \(-\)0.00365 & \(-\)0.00094 & 0 & \(-\)0.00023 & 0.00022 \\
5 & 0.00717 & \(-\)0.00608 & \(-\)0.00108 & 0 & \(-\)0.01346 & 0.01345 \\
6 & 0.00642 & \(-\)0.00534 & \(-\)0.00107 & 0 & \(-\)0.01201 & 0.01201 \\
7 & 0.00757 & \(-\)0.00555 & \(-\)0.00201 & 0 & \(-\)0.01596 & 0.01696 \\
8 & 0.00556 & \(-\)0.00439 & \(-\)0.00117 & 0 & \(-\)0.01285 & 0.01284 \\
9 & 0.00548 & \(-\)0.00449 & \(-\)0.00099 & 0 & \(-\)0.01406 & 0.01307 \\
10 & 0.00416 & \(-\)0.00414 & \(-\)0.00003 & 0 & \(-\)0.00907 & 0.00907 \\
11 & 0.00566 & \(-\)0.00426 & \(-\)0.00140 & 0 & \(-\)0.01224 & 0.01224 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Compensated joint angles \(\Delta q\) (under 18.0 N).
## 5 Implementation and Caveats
### Computational Delay  
The computation of the discriminator and the loop filter consumes a certain amount of time which is defined as the computational delay as described earlier. Proper buffering of the incoming signal samples can provide the necessary delay to allow an NCO update for the next integration interval. If buffering is not possible, the NCO update must eventually be delayed by one integration period (i.e., \(t_{\text{D}}=T\)). The effect of the computational delay is shown in Figure 12 for the second-order DPLL with SI/SI integration, using \(BT=0.26\) which has nearly zero margins when \(t_{\text{D}}=T\) (\(BT_{\text{NCO}}=0.27\)). As expected, the result of the NCO update delay case oscillates while the immediate update case does not. One can observe the actual degradation of stability by the computational delay. Furthermore, the result verifies that the obtained \(BT\) upper limit by root loci in Section 3 is correct.  
Figure 11: Carrier phase tracking error for \(T=20\) ms, a 20 Hz second-order DPLL with SI/SI numerical integration, \(t_{\text{D}}=0\), \(C/N_{0}=47.7\) dB-Hz, and two different carrier phase reference epochs.  
Figure 12: Carrier phase tracking error for \(T=20\) ms, a 13 Hz second-order DPLL with SI/SI numerical integration, \(C/N_{0}=47.7\) dB-Hz, and immediate NCO update (\(t_{\text{D}}=0\)) and an NCO update delay of 20 ms (\(t_{\text{D}}=T\)).
## 3 Results and Discussion
### Gas Recovery Efficiency and Interlayer Compatibility
#### 3.3.2 Interference Index and Commingled Production Loss Ratio
The observation reveals that, during a commingled multilayer production, an increase in interlayer differences leads to a higher interference index, resulting in a greater loss ratio of commingled production gas rates and a reduced proportion of gas production from the low-pressure layer. When the pressure difference of the commingled layers is similar, an increase in the number of commingled layers will result in an escalation of interference time, backflow gas volume, and backflow rate. Consequently, this amplifies the degree of interference, ultimately leading to a decline in overall gas production and an elevation in commingled production gas loss ratio [23; 24; 25].
## 2 Materials and Methods  
### Sample Preparation  
The pathogenic fungi _C. fimbriata_ was provided by the Xuzhou Academy of Agricultural Sciences in Xuzhou, China. In a sterile environment, _C. fimbriata_ was inoculated on potato dextrose agar (PDA) and cultured at 28 \({}^{\circ}\)C and 85% relative humidity for 7 to 14 days. The spores on the surface of the medium were rinsed with sterile saline solution (0.85% NaCl; all solution concentrations are volume/volume). Then, the spore suspension was collected in a conical flask and counted with a haemocytometer under a microscope. The spore suspension was adjusted to a final concentration of 1 \(\times\) 10\({}^{6}\) spores mL\({}^{-1}\) with sterile saline solution.  
Sweetpotatoes of the variety \"Xinxiang\" were provided by the sweetpotato planting base of the crop research institute at Zhejiang A&F University in Hangzhou, China. Fresh sweetpotatoes of the same colour and size without diseases or insect pests were selected. The sweetpotatoes were first washed with tap water and soaked in 1% sodium hypochlorite solution for 2 min to disinfect them, then rinsed with purified water, and, finally, 75% ethanol solution was used to disinfect the surface of the sweetpotatoes and they were dried. Six holes were punched evenly in the equatorial part of the sweetpotatoes; each hole was 2 mm in diameter and 5 mm in depth. All holes were inoculated with 10 \(\upmu\)L of spore suspension. After the spore suspension was absorbed, sweetpotato samples were placed in a sterilized incubator at 28 \({}^{\circ}\)C and 80% relative humidity and taken for detection at 0 h, 8 h, 16 h, 24 h, 32 h, 40 h, 48 h, 56 h, 64 h, and 72 h. Sweetpotatoes inoculated with sterile saline solution at 0 h were used as control.
# Self-Supervised Learning across the Spectrum
## 6 Experiments and Results
### Qualitative Evaluation  
In Figure 5, we plot an example of segmentation results of S4 from the test set of the PASTIS-R dataset from models trained with 100% of the labels. Images from the first two rows show model results with optical inference. Images from the last two rows show model results with radar inference. Qualitatively, we can visualize our model's ability to benefit from supervision, as we can segment hard class labels such as the light green ones with very few training examples.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ablation** & **Inference Modality** & **mIoU** \\ \hline SL 10\% & Optical & 33.7 \\ SL 10\% & Radar & 36.5 \\ SL 100\% & Optical & 53.4 \\ SL 100\% & Radar & 54.6 \\ \hline DL 2 10\% & Optical & 29.5 \\ DL 10\% & Radar & 33.4 \\ DL 100\% & Optical & 48.7 \\ DL 100\% & Radar & 51.9 \\ \hline \hline \end{tabular} 1 short for Same Location pretraining. 2 short for Different Location Pretraining.  
\end{table}
Table 6: Geographical Ablation Results.  
Figure 5: Qualitative results on optical inference. Each row represents a different sample or geographic location from the PASTIS-R dataset for S4’s evaluation. The first column (**leftmost**) is a single optical image from the optical SITS. The second column is a single radar image from the radar SITS. The third column is the prediction from S4. The fourth column (**rightmost**) is the ground truth segmentation map.  
In Figure 6, we plot example segmentation results of S4 from the test set of the Africa Crop Type Mapping dataset from models trained with 100% of the labels. Just as in Figure 5, the rows show different samples and the columns show the model inputs, outputs, and labels. Note that in this set of visualizations, we omit the background class due to the high background label class imbalance of the dataset. On this dataset, we can examine S4 can largely identify the correct class for most of the pixels associated with the relevant agricultural parcel.
## 4 Materials and Methods
### Evaluation Indices and Networks Hyper-Parameters  
#### 4.3.1 Evaluation Indices  
To consider the efficiency of the suggested CAD, four indices were computed: True Positive (_TP_), True Negative (_TN_), False Negative (_FN_), and False Positive (_FP_). Such indices describe the example numbers that are perfectly or incorrectly recognized as positives or negatives. The obtained indices are used to compute evaluation metrics, such as sensitivity, specificity, accuracy, F1-score, precision, and Mathew correlation coefficient (MCC). The following equation describes the evaluation metrics:  
\[Accuracy=\frac{TP+TN}{TN+FP+FN+TP} \tag{18}\]  
\[Sensitivity=\frac{TP}{TP+FN} \tag{19}\]  
\[Precision=\frac{TP}{TP+FP} \tag{20}\]  
\[MCC=\frac{TP\times TN-FP\times FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}} \tag{21}\]  
\[F1-Score=\frac{2\times TP}{(2\times TP)+FP+FN} \tag{22}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{23}\]
## 3 Analysis of Experimental Data and Results
### Probabilistic Model Development
#### 3.3.2.1 Model Error Estimation
The regression between \(log(da/dN)\) and \(log(dc/dN)\) for the experiments and confidence interval are depicted in Figure 8. As expected, the model parameters are considerably different between AI7075-T6 and Ti-6-4 testing, but the form of the model remains unchanged. To provide a visual comparison, the linear regressions between crack growth rate and AE count rate and the raw data for AI7075-T6 and Ti-6-4 are displayed in Figure 9.  
Some inferences about AE phenomena in different materials can be made by comparing the regression parameters listed in Table 3. The slope parameter, \(\alpha_{1}\), for the Ti-6-4 is smaller than the reported \(\alpha_{1}\) value for AI7075-T6. This relationship suggests that as the AE count rate increases, less increase is observed in the crack growth rate in the Ti-6-4 as compared to AI7075-T6. The intercept parameter, \(\alpha_{2}\), for the Ti-6-4 is larger than AI7075-T6. Physically, this means that a crack will generally grow at a faster rate in the Ti-6-4 than AI7075-T6 for the same relative AE count rate. In other words, as a crack propagates, higher crack growth rates are detected for the Ti-6-4 than AI7075-T6. The tradeoff when detecting crack growth through means of observing AE count rate is a crack will grow more quickly but at a relatively consistent rate for Ti-6-4, compared to a slower but relatively variable crack growth rate in AI7075-T6. Finally, it should be noted that the differences in the crack growth models are simplified into two dimensions, the two model parameters, \(\alpha_{1}\) and \(\alpha_{2}\). If the relationships between  
Figure 8: Posterior predictive AE model with the uncertainty bounds, material: Ti-6 Al-4V, WB indicates Bayesian regression analysis results using WinBugs. Figure 9: Crack growth rate versus AE count rate with linear regressions for AI7075-T6 and Ti-6 Al-4V. \(log(da/dN)\) and \(log(dc/dN)\) for Ti-6-4 were not linear, at least one other material-dependent dimension would need to be considered.
###### Abstract  
In recent years, joint entity-relation extraction (ERE) models have become a hot research topic in natural language processing (NLP). Several studies have proposed a span-based ERE framework, which utilizes simple span embeddings for entity and relation classification. This framework addresses the issues of overlap and error propagation that were present in previous entity-relation extraction models. However, span-based models overlook the influence of lexical information on the semantic representation of the span and fail to consider relations with a strong intrinsic connection between span pairs. To tackle these aforementioned issues, we present a new ERE model called ER-LAC (Span-based Joint Entity and Relation Extraction Model with Multi-level Lexical and Attention on Context Features). This model is designed with multi-granularity lexical features to enhance the semantic representation of spans, and a transformer classifier is employed to capture the internal connections between span pairs, thereby improving the performance of relational classification. To demonstrate the effectiveness of the proposed model, ablation experiments were conducted on the CoNLL04 dataset. The proposed model was also compared with other models on three datasets, showcasing its computational efficiency. The results indicate that the introduced lexical features and classifier enhance the F1 score for entity extraction by 0.84% to 2.04% and improve the F1 score for relationship classification by 0.96% to 2.26% when compared to the previous state-of-the-art (SOTA) model and the baseline SpERT model, respectively.  
named entity recognition; relation extraction; lexical features; internal connections; natural language processing  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]-RAC: Span-Based Joint Entity and Relation Extraction Model with Multi-Level Lexical and Attention on Context Features. _Appl. Sci._**2023**, _13_, 10538. [[https://doi.org/10.3390/app131810538](https://doi.org/10.3390/app131810538)]([https://doi.org/10.3390/app131810538](https://doi.org/10.3390/app131810538))
## 4 Optimization of Measurement Parameters
### Demodulation Method  
To determine the detection current of each component, the absorption lines of the three gases in the full dynamic range of the ICL are investigated (Figure 6). At an ICL temperature of 20 \({}^{\circ}\)C, the injected current is modulated at the resonance frequency (710 Hz), and the resulting DHR photoacoustic signal is demodulated at 1f (Figure 6a) and 2f (Figure 6b). The background signal is collected with high purity nitrogen, and the final signal is obtained by subtracting the background signal from the sample gas signal. The study shows that when 2f demodulation is used, the response signals of ethylene and methane are almost in reversed phase (blue and violet regions in Figure 6b), which will cancel each other and is very unfavorable for detection. However, with the 1f demodulation mode, although the signals  
Figure 4: Frequency response of the DHR photoacoustic cell (50 ppm methane under ambient pressure).  
Figure 5: (**a**) Injection current scanning profile; (**b**) Variation of the 1f signal with the modulation depth.  
of CH4 and C2H4 also cancel each other in the injection current range of 69.5-71.4 mA (blue region in Figure 6a), a detection window arises for ethylene when the injection current is at 73.35-79.83 mA (violet region in Figure 6a). Thus, the demodulation method with 1f is adopted for signal acquisition. Since the first harmonic signal is affected by the residual amplitude modulation (RAM), the RAM will produce a background signal with a superimposed absorption signal, which is different from the second harmonic (without background signal), and the working curve will have a nonnegligible intercept [38].
## 5 Conclusions  
This paper proposes and analyzes a new MC\(-\)ODBR\(-\)DCSK system and deduces its energy efficiency. Simulation results show that this system has better energy efficiency compared to MC\(-\)DCSK and DCSK systems, using multicarrier transmissions and two orthogonal reference chaotic signal modulations to achieve higher data rates. In addition, when the chaotic signal and the number of packets change, the bit error rate performance hardly changes. Therefore, the system has good confidentiality. Currently, we have studied the software simulation of the MC\(-\)ODBR\(-\)DCSK scheme, and in the future, we hope to implement the work of chaotic communication signal generation and modulation schemes using a Software Radio Platform based on High\(-\)Performance USRP. The MC\(-\)ODBR\(-\)DCSK system is promising and demonstrates great potential for the future of high-data-rate communication systems.  
Conceptualization, Y.F.; data curation, X.L.; investigation, Q.J.; methodology, T.S.; writing--original draft, B.Q. and F.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported in part by the National Natural Science Foundation of China (Grant No. 61971291), the central government leading local science and technology development projects (2022020128-JH6/1001), Shenyang Natural Science Foundation (Grant No. 22-315-6-10), and the science and technology funds from Liaoning Education Department (serial number: IJKZ0242).  
The authors declare no conflict of interest.
# A Multimode Fusion-Based Aviation Communication System
## 3 Hardware Design
### BeiDou Satellite Communication Module  
The BeiDou satellite communication system is a two-way messaging service that relies on the BeiDou satellite navigation system. It offers communication services through the L-band and S-band links of the GEO satellites in the BeiDou constellation. The system comprises a space segment, a ground control segment, and a user segment. It boasts of a wide communication range and strong anti-interference capabilities, as illustrated in Figure 7. The communication functions of the BeiDou satellite link include:  
Figure 5: The 5G public network communication link diagram.  
Figure 6: The 5G public network module composition block diagram.  
The aircraft control computer collects flight status data that are transmitted to the ground control center using the BeiDou satellite communication link.  
Ground control commands are sent to the aircraft control computer via the BeiDou satellite communication link.  
The circuit diagram of the BeiDou satellite communication module is shown in Figure 8.  
This module consists of the RDSS module and B1 & L1 positioning module. The RDSS module, with high integration and low power consumption, integrates the BeiDou RDSS RF transceiver chip, RDSS baseband chip, 5 W power amplifier chip, and LNA internally. The BeiDou SIM card and passive antenna can be connected externally to achieve the short message communication function of BeiDou RDSS. The B1 & L1 positioning module is used for auxiliary positioning. The standby power consumption of this module is about 2 W, and the signal transmitting power is about 15 W.