esa-sceva/satcom-chunk-collection huggingface dataset:
# A Local Information Perception Enhancement-Based Method for Chinese NER
## 4 Experimental Results and Comparison
### Overall Results
Compared with the Resume dataset with eight types of entities, our model's F1 value is 0.13% lower than PGAT-BERT [27], ranking second among all baseline models. Finally, the performance on the OntoNotes dataset is also better than the baseline model, which means that the model can also handle larger datasets. Our model gives the Chinese NER project fresh insights while improving the entity identification performance simply and effectively. To evaluate the model's performance on real-world datasets, we also consider the Youku dataset, which consists of video titles from Youku.com [39]. We use the same hyperparameter settings as for the Weibo dataset. [PERSON] crawled these data from the Youku video site and manually annotated such data with named entities. They present a novel but easy-to-implement method for identifying named entities with incomplete data annotations. We choose the model in which all entities are retained as the baseline. The results of the experiment can be seen in Table 5. The experimental results in the above table show that the algorithm proposed in this paper obtains better performance than the baseline algorithm. This indicates that our method equally applies to real industrial application scenario datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **P** & **R** & **F1** \\ \hline Bi-LSTM-CRF & 71.1 & 76.1 & 73.6 \\ (+N-gram features) & 71.2 & 75.9 & 73.5 \\ (+PIET features) & 71.7 & 75.8 & 73.7 \\ (+PDET features) & 72.6 & 75.1 & 73.8 \\ \hline Multi-Graph [37] & 74.3 & 76.2 & 75.2 \\ PGAT-BERT [27] & 79.7 & 81.7 & 80.7 \\ ours & 81.6 & 82.1 & 81.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Precision rate, recall rate, and F1 score statistics on the E-commerce dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **P** & **R** & **F1** \\ \hline Baseline & 83.0 & 81.7 & 82.4 \\ Ours & 87.0 & 85.1 & 86.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Precision rate, recall rate, and F1 score statistics on the Youku dataset.
# Condensation Flow of Refrigerants Inside Mini and Microchannels: A Review
## 3 Condensation Flow of Refrigerants Inside Mini/Micro-Channels
### Experimental Studies Focused on Mini/Microchannel Condensation of Refrigerants
They explored the impact of flow conditions (mass flux, vapour quality, saturation temperature, and coolant inlet temperature), channel diameter, channel geometry, and thermophysical properties on the heat transfer coefficients. Furthermore, [PERSON] et al. [56] carried out an experimental study on the pressure drop characteristics of R32, R152a, and R22 during condensation under the same experimental conditions. Their findings revealed that pressure drops increase with rising mass flux and vapour quality and decreasing saturation temperature and channel diameter. However, frictional pressure drops exhibited insensitivity to coolant conditions. In a separate investigation, [PERSON] et al. [57] presented empirical data on heat transfer and pressure drop for the condensation of propane, R1234 ze (E), and R22 in both circular (with D\({}_{\text{h}}\) = 1.085 mm) and square (with D\({}_{\text{h}}\) = 0.952 mm) horizontal microchannels. Their experiments, conducted at saturation temperatures of 40 and 50 \({}^{\circ}\)C, encompassed a range of mass fluxes from 200 to 800 kg/m\({}^{2}\)s and vapour qualities from 0.1 to 0.9. This study sought to explore the influences of mass flux, vapour quality, saturation temperature, and channel geometry on heat transfer coefficients and pressure drops. Furthermore, [PERSON] et al. [58] conducted an experimental investigation to analyse the impact of refrigerant mass flux, local vapour quality, coolant flow rate, and inlet coolant temperature on local condensation heat transfer coefficients. Their study employed rectangular multi-microchannels with a hydraulic diameter of 0.57 mm, utilising HFE-7100 as the refrigerant. Flow visualisation techniques were also employed to capture the flow patterns during the condensing process. The recent experiments investigating the condensation of refrigerants in mini- and microchannels are summarised in Table 5.
## 4 Discussion
### Salt Stress Induces the Accumulation of Bioactive Compounds in D. officinal Leaves  
Jasmonates (JAs), such as methyl jasmonate (MeJA), are described as small signaling molecules in plants, and are commonly used to induce gene expression involved in plant stress responses and enhance production of secondary metabolites [40]. Some studies have used MeJA to promote the accumulation of bioactive compounds (polysaccharides, alkaloids, and flavonoids) in protocorm-like bodies of _D. officinal_[41; 42]. JA accumulates within less than 120 s in wounded _Arabidopsis_ leaves [43]. After 2 h of salt-stress treatment, the JA content in _D. officinal_ leaves also increased significantly (Figure 8A). When plants are exposed to environmental stress, they generate JAZ-Ile at concentrations as low as 50 nM, which significantly promotes the interaction between COI1 and JAZ proteins [44]. Afterwards, JAZs subject to degradation by the 26S proteasome, and the released transcription factors (TFs) activate the expression of JA-responsive genes [45]. Flavonoids are valuable natural products derived from the phenylpropanol pathway [46]. The MBW complex formed by the interaction R2R3-MYB, basic helix-loop-helix (bHLH) and WD40 proteins usually regulates gene expression involved in the flavonoid biosynthesis pathway [47; 48]. In our results, the contents of Ile and JA increased significantly under salt stress (Figures 5A and 8A), and the expression level of _DobHLH_ (_gene9655_) (a homolog of _AtMYC4_) and _DoMYB_ (_gene6015_) (a homolog of _AtMYBTT2_) were downregulated and up regulated, respectively, under salt stress. Furthermore, salt stress induced the accumulation of flavonoids such as rutin in _D. officinal_ leaves (Figure 5B). Hence, we hypothesized that the interaction between JAZ and DobHLH (gene9655) would inhibit JA response under normal circumstances. In contrast, salt stress could trigger JA biosynthesis, and synthetic JA-Ile would induce the interaction of COI1 with JAZ proteins, leading to the degradation of JAZ proteins via the 26S proteasome, and DoMYB (gene6015) was released to activate the expression of genes involved in flavonoid biosynthesis (Figure S7). Therefore, cultivating _D. officinal_ plants in saline-alkali soil may be beneficial to increase the content of secondary metabolites (such as flavonoids) in leaves.
## 2 Related Works
### Task Offloading in LEO Satellite Networks  
In scenarios involving task offloading in LEO satellite networks, references [17, 18] suggest energy-efficient algorithms for task offloading. The work in [17] examines a ground network supported by LEO satellites and offers a cloud-edge collaborative offloading strategy that accounts for service quality constraints, facilitating efficient energy offloading within a satellite network. It provides separate solutions utilizing deep reinforcement learning and game theory. Meanwhile, [PERSON]. [18] explores a hybrid cloud-edge LEO satellite computing network with a three-layer structure, focusing on reducing total energy consumption for ground users. This research applies game theory to devise task offloading strategies for each ground user.  
Studies [19, 20, 21] introduce algorithms that optimize delay in task offloading. Ref. [19] presents a network architecture integrating space and ground components to deliver edge computing services to LEO satellites and UAVs, focusing on reducing task execution delays through deep reinforcement learning for offloading decisions. Ref. [20] examines task offloading challenges within modern small satellite systems, proposing a strategy that optimizes delay while offloading and dismantling tasks. For satellite relay computing, [PERSON]. [21] suggests a collaborative computing approach between satellite and ground to specifically reduce task response delays.  
Other works [22, 23, 24, 25] focus on creating energy-efficient and delay-optimized strategies for task offloading. Ref. [22] analyzes the time delays inherent in satellite-ground communications within relay computing scenarios, proposing a heuristic search algorithm to balance the computational load and reduce system energy consumption and task execution delay. Ref. [23] introduces a task offloading method employing a heuristic search algorithm specifically to lower both energy consumption and task execution delay for ground users. Ref. [24] also focuses on modern small satellite systems, suggesting a delay-optimized strategy for both offloading and dismantling tasks. Ref. [25] proposes a local search matching algorithm to determine the optimal task offloading strategy, targeting a reduction in energy usage and delay for ground users.
# Automatic Hybrid Attack Graph (AHAG) Generation for Complex Engineering Systems +
## 2 Model-Based Attack Graph Implementation
### Formal System Description for Networked Examples  
The generation of Attack Graph requires an overall formal description of the system model and security property being investigated, encoded in Architecture Analysis and Design Language (AADL) and checked using JKind. Here, the formal descriptions of \(CN_{1}\) and \(CN_{2}\), respectively, are given as follows [20].  
1. Set of Routers R = 1, 2, 3, IP Cloud; Variable \(I\in\{1,\,2,\,3,\,4\}\) (static parameters).
2. Set of LANs N = 1, 2, 3, 4; Variable \(k\in\{1,\,2,\,3,\,4\}\) (static parameters).
3. Set of Service Providers S; Variable \(s\in\{\)Ftp, Email, Video\(\}\) (static parameters).
4. Set of Connection Links L \(\subseteq\) R \(\times\) R, R \(\times\) N, R \(\times\) S; Labeled \(\mathrm{l_{ij}}\equiv\) Link is placed between component i and component j (static parameters).
5. System Connectivity C = L; Boolean cjj = 1 if there is a connection between component i and component j (dynamic variables).
6. System Stability T; Boolean t = 1 if system is stable (dynamic variable).
7. System Performance P \(\subseteq\) S; Boolean fk = 1 if ftp service is provided to LAN k, Boolean ek = 1 if Email service is provided on LAN k and Boolean vk = 1 if Video service is provided on LAN k (dynamic variables).
8. System recovery Action R; Variable \(r\in\{\)p, a, d\(\}\), in case of normal operation \(r\) = p, in case of recovery action \(r\) = a, and in case of no action can be done, \(r\) = d (dynamic variables).
9. Number of faulted Links that occur sequentially N; Variable \(n\in\{0,1,\,2\}\), in case of no fault \(n\) = 0, in case of first fault \(n\) = 1, and in case of second fault \(n\) = 2 (dynamic variables).
10. Attack Instance AI \(\subseteq\) A \(\times\) R \(\times\) R, A \(\times\) R \(\times\) N, A \(\times\) R \(\times\) S, Labeled ajj\({}^{\text{m}}\equiv\) Attack a on the Link between component i and component j, where m \(\subseteq\) L is a sequence of the previous faulted link(s) if exists. (static parameters)
11. Pre-Attack conditions for CN1:  
Figure 1: (a) Network \(CN_{1}\) and (b) Network \(CN_{2}\).
## 4 Inertia Identification of Wind Farms Based on Analytical Optimization
### Analytical Inertia Identification of Wind Farms  
Following the identification of the wind farm's control structure and key parameters via the SR3 algorithm, the equivalent virtual inertia is quantitatively evaluated using the analytical expression derived in Section 2. Specifically, the identified virtual inertia parameters, low-pass filter time constant and so on are substituted into the expression to obtain the equivalent virtual inertia under the given control strategy.  
This method bridges the data-driven identification results with theoretical models, ensuring both physical consistency and interpretability. It enables a straightforward and accurate evaluation of the wind farm's inertial support capability under different control strategies.
## 3 Bridge Construction Quality Evaluation Method and Evaluation Process
### Combinatorial Weighting Method-TOPSIS System Evaluation Process  
1. Firstly, according to various factors affecting bridge construction quality, suitable evaluation indexes were selected to establish a three-layer mega bridge across the Yellow River construction quality evaluation index system. The scoring value of each construction process evaluation index was calculated by the specification \"Technical Condition Assessment Standard for Highway Bridges;\"
2. Objective weight values were calculated using the CRITIC method. Firstly, the scoring values of each construction process evaluation index obtained in the previous step were standardized. The means and standard deviations of the standardized data were obtained, and the comparison intensity was calculated according to Equation (5). The weight values were calculated according to Equations (6)-(9);
3. Subjective weight values were calculated using the IAHP method. A two-by-two comparison of each evaluation index was performed to obtain the comparison judgment matrix. According to Equations (11)-(13), the antisymmetric matrix, optimal transfer matrix, and proposed optimal consistent matrix were calculated step by step, and the proposed optimal consistent matrix was normalized. Finally, the subjective weight values were calculated using Equations (15) and (16);
4. The combined assignment method enables combination of the advantages of subjective assignment and objective assignment, and the subjective and objective weight values obtained in steps (2) and (3) are brought into Equation (18) to obtain the final combination weight;
5. TOPSIS method evaluation. After the weight values were calculated through steps (2)-(4), the final evaluation of bridge construction quality was performed according to the TOPSIS method. After normalizing the scoring criteria of the construction quality evaluation index of the mega bridge across the Yellow River, the weighted decision matrix of the scoring criteria was obtained according to Equation (20). According to Equations (21) and (22), positive and negative ideal solutions are obtained. Then, from Equations (23) and (24), the distance values of each solution to the positive and negative ideal solutions are calculated. Finally, the relative closeness interval of each evaluation category is calculated by Equation (25). Based on the normalized data of the evaluation index scores, the above steps are repeated to calculate the relative closeness of the construction quality of the mega bridge across the Yellow River; the final construction quality evaluation category is obtained by judging the interval in which it is located.
# Recycled Polyethylene Fibres for Structural Concrete
## 1 Introduction
According to UNE-EN-14889-2 [35], polymeric fibres can be classified into two groups depending on their diameter: microfibres (less than 0.30 mm in diameter) or macrofibres. The latter are considered structural fibres. The influence of polymeric recycled fibre on concrete properties have been studied in the last decade for several types of plastic, such as PET, PP, PVC or PE. A brief review of the mechanical properties of fibre-reinforced concrete has been carried out. Regarding compressive strength, different results can be observed; some researchers reported an enhancement of this property (PET [36; 37], PP [38; 39], PVC [40; 41], PE [42; 43; 44]). However, other studies presented a clear reduction in strength (PET [45; 46], PVC [47], PE [48; 49]). Regarding flexural strength, all authors agree that it increases as fibres are added, this improvement occurs for a relatively low fibre content (approximately less than 1% fibre volume) (PET [36; 37; 45; 46; 50; 51], PP [38; 39; 52; 53], PVC [40; 41; 47], PE [42; 43; 44; 48; 49]). In this context, this research involves the evaluation of out-of-use black polyethylene tubes for their recycling and subsequent manufacture of reinforcing fibres, which will be used for concrete. Section 2 explains the methodology (including the recycling process and the manufacture of the fibres and the concrete mix), together with a description of the experimental program. Section 3 reports the experimental results and the corresponding analyses, and Section 4 presents the main conclusions.
## 5 Conclusions
### Influence of Temperature and Precipitation  
While this study finds that the influence of temperature and precipitation on NDVI changes in Aksu appears limited, further analysis suggests several underlying reasons for this outcome. One potential reason is the inherent resilience of Aksu's vegetation types to climatic fluctuations, given the arid conditions and relatively stable, drought-adapted plant communities in the region. Vegetation in arid environments like Aksu has often evolved to withstand variability in temperature and precipitation, relying on deep root systems and other adaptive mechanisms to maintain stability despite external climate changes.  
Additionally, Aksu's vegetation dynamics may be more significantly impacted by non-climatic factors, such as human-driven activities (e.g., urban expansion, agricultural practices) that directly influence vegetation cover and health. For instance, nighttime light data as a proxy for urbanization revealed that human activities play a prominent role in shaping vegetation patterns, potentially overshadowing the effects of temperature and precipitation changes.  
To deepen the understanding of this limited climatic influence, future research should examine other climate variables--such as soil moisture, wind, and evapotranspiration--that may play a more direct role in influencing vegetation in arid regions. Furthermore, distinguishing the responses of specific vegetation types (e.g., shrubland vs. grassland) to these factors could yield a more nuanced perspective, as different plant communities may exhibit varying sensitivities to environmental changes. Expanding the analysis to consider these additional factors would provide a more comprehensive picture of the climatic and non-climatic drivers of vegetation dynamics in Aksu and similar arid regions.
# Signal Design and Compatibility Assessment for LEO Navigation Augmentation System +
## 4 Compatibility Assessment
Meanwhile, the receiving power of the GNSS signal is also calculated based on the minimum value, as shown in Table 1.  
Figure 3: Average number of visible satellites during 24 h of GNSS system: **(a)** GPS, **(b)** Galileo, and **(c)** BeiDou-3. For L1 signals, the considered GNSS signals include L1 C/A (BPSK (1)) and GPS L1C/Galileo E1 OS/BDS B1C (MBOC(6,1,1/11)), with a signal receiving bandwidth of 14.322 MHz. For the L5 signal, the considered GNSS signals include GPS L5/Galileo E5a/BDS B2a (QPSK (10)), with a signal receiving bandwidth of 20.46 MHz. The considered LEO navigation augmentation signals include BOC (2,1), BOC (2,2), BOC (4,1), and BOC (4,2). To simplify the calculation, assuming that the transmission bandwidth is consistent with GNSS, the calculated SSC is shown in Table 3.  
It is assumed that the receiving power on the ground of LEO navigation signals is calculated based on the maximum power of the GNSS signal of \(-154\) dBW and \(N_{0}=-204\) dBW/Hz. The analysis results of the degradation of effective \(C/N_{0}\) at L1 and L5 are shown in Figures 4 and 5, respectively. The results show that as the number of visible LEO satellites increases, the effective \(C/N_{0}\) of GNSS signals decreases. BOC (4,1) and BOC (4,2) have better compatibility compared to BOC (2,1) and BOC (2,2), resulting in a smaller degradation in the effective \(C/N_{0}\) of GNSS signals. As the number of visible LEO satellites increases, the degradation in the effective \(C/N_{0}\) of GNSS signals increases. It is necessary to design LEO constellations reasonably to avoid unacceptable effects on GNSS signals. Figure 4: Analysis results of the degradation of effective \(C/N_{0}\) at L1: (a) GPS L1C/A, (b) GPS L1C, (c) Galileo E1 OS, and (d) BDS B1C.
## 4 Orbit-Tracking and -Determination Algorithm Simulation  
### Accuracy of Clock Difference Calculations  
Extended users, constrained by weight and power consumption, cannot be equipped with high-precision atomic clocks. The initial error of the clock offset parameter a0 can reach 5 ms, and that of a1 can reach 5 kHz, with the time-frequency accuracy being two to three orders of magnitude worse than that of navigation satellites. These extended users rely on their own wide-range capture capabilities to establish unidirectional communication links with navigation satellites, connecting with different satellites each minute. We select the latest 20 sets of unidirectional test data from the past 5 min for preprocessing, then correct the observational values, linearize them and form observation equations. After estimating the parameters of the observation equations, we obtain the initial state of the satellites and the satellite clock offset parameters. The optimal estimation of these parameters is achieved through residual calculation and iteration, as shown in Figures 4 and 5. It can be seen that the solution accuracy for a0 is better than \(5\times 10^{-7}\) seconds, equivalent to 0.5 microseconds (us), and the solution accuracy for a1 is better than \(1.35\times 10^{-9}\), equivalent to 31 hertz (Hz), meeting the time-frequency synchronization requirements at the microsecond and tens-of-hertz levels. The time accuracy has been improved by three orders of magnitude, and the frequency accuracy by two orders of magnitude. This method solves the problem of non-atomic clock users achieving high-precision time-frequency synchronization with fewer time slot resources and navigation satellite links, providing a new solution for time-frequency synchronization for other users.