esa-sceva/satcom-chunk-collection huggingface dataset:
# Node-Based Optimization of GNSS Tomography with a Minimum Bounding Box Algorithm
## 3 New Node-Based Tomographic Approach
### Construction and Solution of Tomographic Equations  
Next, the tomographic equations are introduced to establish the relationships between the tomographic observations and tomographic nodes. These tomographic equations are solved by the algebraic reconstruction technique (ART). ART is used in computed tomography and it is also successfully applied to GNSS tomography [30]. ART is considered an excellent iterative solver of a system of linear equations since its high numerical stability, even under adverse conditions.  
The procedure for the construction of the tomographic equations includes four main steps: (1) The SWV quantity in the tomographic observation can be expressed by section integration. The integration paths (parts of the ray path) of which are divided by the adjacent tomographic planes. (2) Each of these section integrations is first estimated approximately by the Newton-Cotes formulas (i.e., formulas for quadrature based on evaluating the integrand at equally spaced points). (3) The mathematical relationship between the equally spaced points and tomographic nodes is established by the distance inverse weight method and vertical interpolation function in both the horizontal and vertical direction, respectively. Finally, the tomographic equations are constructed to link the tomographic observations and the tomographic nodes. These steps are elaborated in the [PERSON] et al. [31] and the tomographic equations are defined by  
\[\text{NX}=\text{B} \tag{5}\]  
where the N is the coefficient matrix of the tomographic model; X is the vector of the unknown parameters of water vapor densities; B is the vector of the SWVs. The access order scheme based on prime number decomposition (PND) and ART [19] is used to define the iteration sequences of (5) and then to estimate the tomographic results.
# Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events
###### Abstract  
Compressive sensing is a simultaneous data acquisition and compression technique, which can significantly reduce data bandwidth, data storage volume, and power. We apply this technique for transient photometric events. In this work, we analyze the effect of noise on the detection of these events using compressive sensing (CS). We show numerical results on the impact of source and measurement noise on the reconstruction of transient photometric curves, generated due to gravitational microlensing events. In our work, we define source noise as background noise, or any inherent noise present in the sampling region of interest. For our models, measurement noise is defined as the noise present during data acquisition. These results can be generalized for any transient photometric CS measurements with source noise and CS data acquisition measurement noise. Our results show that the CS measurement matrix properties have an effect on CS reconstruction in the presence of source noise and measurement noise. We provide potential solutions for improving the performance by tuning some of the properties of the measurement matrices. For source noise applications, we show that choosing a measurement matrix with low mutual coherence can lower the amount of error caused due to CS reconstruction. Similarly, for measurement noise addition, we show that by choosing a lower expected value of the binomial measurement matrix, we can lower the amount of error due to CS reconstruction.  
compressive sensing; transient photometry; compressive sensing noise +
Footnote †: journal:  
[PERSON] [PERSON]; [PERSON]; [PERSON] Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events. _Sigust_**2022**, 3, 794-806. [[https://doi.org/10.3390/sigust3040047](https://doi.org/10.3390/sigust3040047)]([https://doi.org/10.3390/sigust3040047](https://doi.org/10.3390/sigust3040047))
## 2 Related Work
Based on the above analysis, it is found that English-language network security knowledge graph construction techniques are relatively mature, but research on Chinese-language network security knowledge graphs is still in its early stages. Due to significant differences between Chinese and English languages, existing techniques for constructing English-language networks cannot be directly applied to Chinese-language network security knowledge graph construction. There is a lack of Chinese-language named entity recognition methods specifically designed for the Chinese environment in network security NER. To address this, this paper proposes a CSBERT-IDCNN-BiLSTM-CRF method for Chinese-language network security NER, with the following main research work. 1. From a model perspective, using the CSBERT pre-trained model based on network security can project input representations into the network security semantic space, greatly improving the performance of neural network models on small datasets. By utilizing IDCNN and BiLSTM, the model is endowed with the ability to capture long dependency features and local features, combining these two types of features to better accomplish entity recognition tasks in the field of network security. 2. A reliable dataset is constructed. Currently, most knowledge graphs related to network security face issues such as insufficient data or outdated Chinese domain data that lags current developments in network security. To address this dataset problem, this paper used China National Vulnerability Database (CNNVD) as a source for constructing a network security dataset centered around vulnerability data. The text corpus for dataset construction consists of vulnerability reports and vulnerability data from CNNVD. Automated scripts are used to crawl vulnerability report data, download XML format vulnerability data directly, and utilize YEDDA [27] tool for data annotation to complete dataset construction. 3. This paper has accomplished the training and testing of the model on its own constructed dataset, mitigating issues associated with poor model training due to low-quality datasets. Experimental results indicate that the proposed CSBERT-IDCNN-BiLSTM-CRF model boasts certain advantages in terms of the efficiency and accuracy of entity recognition.
## 4 Results
### Influence of the Runner Speed Acceleration/Deceleration Ramp  
The first step before performing dynamic measurements is to find the optimal acceleration/deceleration ramp of the runner(s) speed in order to stay in quasi- steady-state operating condition during the measurement process. To this end, the speed of the three recirculating pumps of the test rig has been set and maintained constant at a value of 1500 min\({}^{-1}\) in the case of the microturbine, or 2000 min\({}^{-1}\) in the case of the PAT. Then, for the microturbine, considering a constant runners absolute rotational speed ratio \(\alpha\) = 1, the runners speed was uniformly increased from 1000 min\({}^{-1}\) to 2000 min\({}^{-1}\) and then decreased back to 1000 min\({}^{-1}\). The same procedure was applied in the case of the PAT for the range from 750 min\({}^{-1}\) to 2250 min\({}^{-1}\). In total, 6 (for the microturbine) and respectively 4 (for the PAT) different acceleration/deceleration ramps of respectively 10, 25, 30, 40, 60, 90 and 120 s/1000 min\({}^{-1}\) have been addressed.  
\[\eta^{*}=\frac{\eta_{\rm{h-mec}}\ /\ \rm{h-elec}}{\max(\eta_{\rm{h-mec}}\ /\ \rm{h-elec})}\ [-] \tag{5}\]  
\[\eta^{*\prime}=\eta^{*}-\eta^{*}_{fit}\ [-] \tag{6}\]  
In Figure 6, the resulting influence of the acceleration/deceleration ramp of the runner(s) speed on power and on the efficiency, for fixed inflow conditions, is provided. The efficiency is scaled with the maximum measured value determined from the static measurements (see Equation (5)). One may notice here hysteresis and large measurement errors on the resulting efficiency for speed ramps below 40 s/1000 min\({}^{-1}\).  
Figure 6: Influence of the acceleration/deceleration ramp of the runner(s) speed on the power and on the efficiency during one forth and back cycle at fixed testing inflow conditions: (**a**) variation of the counter-rotating microturbine mechanical power (N\({}_{\rm{p1,2,3}}\) = 1500 min\({}^{-1}\)); (**b**) variation of the PAT electrical power (N\({}_{\rm{p1,2,3}}\) = 2000 min\({}^{-1}\)); (**c**) variation of the counter-rotating microturbine efficiency (N\({}_{\rm{p1,2,3}}\) = 1500 min\({}^{-1}\)); (**d**) variation of the PAT efficiency (N\({}_{\rm{p1,2,3}}\) = 2000 min\({}^{-1}\)).  
Then, the Figure 7 presents the influence of the speed acceleration/deceleration ramp on the magnitude of the efficiency fluctuations (due to measurement errors) \(\eta^{\star}\) compared to the average efficiency obtained by curve fitting (see Equation (6)). Accordingly, for both tested cases, the hysteresis effect tends to become insignificant for speed ramps larger than 40 s/1000 min\({}^{-1}\). The evolution of the resulting standard deviation (STD) of the efficiency fluctuation \(\eta^{\star}_{\text{STD}}\) with the speed ramp, provided in Figure 8 and Table 6 confirms this assertion. Consequently, as an acceptable compromise between the measurement time and the measurement precision (efficiency errors below 1% for both cases) a speed acceleration/deceleration ramp of 60 s/1000 min\({}^{-1}\) has been selected for the further dynamic efficiency measurements.
## 3 Results
### Digital Twin Technology Potential for HMGT
Maintenance teams can use this information to make better decisions about when and how to perform maintenance, and flight crews can use it for operational decisions. Digital twin technology allows for the customization of maintenance and operational strategies based on the specific conditions and usage patterns of each aircraft. This customization leads to more efficient operations and can help optimize fuel consumption, flight paths, and other operational parameters to improve efficiency and reduce costs. From design and manufacturing to operation and decommissioning, digital twins support the entire lifecycle management of an aircraft. Manufacturers can use them to improve design and production, while airlines can use them throughout the operational life of the aircraft to monitor its health and performance, ultimately leading to more sustainable lifecycle management. Digital twins can be used to create realistic training environments for pilots and maintenance crews, allowing them to gain experience with different types of scenarios in a risk-free virtual setting. This training can improve safety and ensure that crews are prepared for a wide range of situations. Digital twins can be integrated with other technologies such as AI, the IoT, and blockchain to create a holistic aviation health management system. AI can be used to analyze data from the digital twin to make predictions and recommendations, IoT devices can provide the real-time data needed to keep the digital twin updated, and blockchain can secure the data and ensure that all records are immutable and traceable. While digital twins are a powerful tool, there are challenges to their implementation. These include the need for high-fidelity data, computational resources to run simulations, and integration with existing systems. Additionally, there are considerations around data security and privacy, particularly when handling sensitive operational data.
## Materials and Methods
### Data Analysis  
Visual3D software (C-motion, Inc.; Germantown, MD, USA) was used to analyze the marker positions and force plate data, which were filtered with a low-pass Butterworth filter with cut-off frequencies of 14 Hz and 50 Hz, respectively. The ankle joint angle was defined using the segment coordinate system for the virtual foot segment, which set the ankle joint angle to zero degrees in the static standing, to be aligned with the segment coordinate system for the shank. The ankle joint moment was calculated using Newton-Euler inverse dynamics with the proximal segment of the shank as the reference segment, which was normalized to each participant's body mass. Ankle joint stiffness was calculated as the change in ankle joint moment divided by the change in ankle joint angle from initial contact to peak dorsiflexion [35]. The DPSI is the composite of the vertical (VSI), anteroposterior (APSI), and medial-lateral (MLSI) components, and was computed following the method of [PERSON] et al. [30] using the customized Visual3D software. The square root of the mean square deviation of force, which was the fluctuation from the baseline along each axis of the force plate, was calculated. The APSI and MLSI were assessed using the fluctuations from 0, and the VSI was calculated using the fluctuation from the subject's body weight. The square root of the sum of the squares of APSI, MLSI, and VSI constituted total DPSI. These variables were calculated using the first 3 s following initial contact, identified as the force threshold exceeding 10 N. The time interval of 3 s is recommended by [PERSON] et al. for studies of sports performance [36]. For anterior single-leg jump landings, the variables of interest included: (1) ankle dorsiflexion ROM, which refers to the total ankle dorsiflexion excursion; (2) ankleevers ROM, which refers to the total ankleeversion excursion; (3) total ankle ROM in the sagittal and frontal planes, which refers to the total angle changes in the ankle joint in both planes; (4) peak ankle plantar flexion moment, which refers to the maximum plantar flexion moment; (5) peak ankle inversion moment, which refers to the maximum inversion moment; (6) ankle joint stiffness; and (7) APSI, MLSI, VSI, and DPSI, which refer to the assessments of dynamic postural stability. For lateral single-leg jump landings, the variables of interest were similar to the anterior single-leg jump landing, but with two extra variables: (1) ankle inversion ROM, which is the total ankle inversion excursion; and (2) peakeversion moment, which is the maximumeversion moment. The variables of interest are listed in Tables 1 and 2.
# An Efficient Local Search Heuristic for Earth Observation Satellite Integrated Scheduling
## 4 An Efficient Local Search Heuristic for Earth Observation Satellite Integrated Scheduling
### A Fast Constraint Evaluation Mechanism  
In [24] a fast constraint verification mechanism was proposed by introducing the concept of Valid Observation Time Interval (VOTI). The VOTI allows fast verification of VTW constraints for insertion operation. We extend this idea in the context of memory constraint and constraint violation so that the constraint can be verified and evaluated with O(1) complexity in case of violation.
## 2 Message Replication in OppNets for Sensed Data Collection
### Fixed-Quota Replication  
Fixed-quota replication is achieved by \"spraying\" a certain number of message copies in the network, and then by utilizing single-copy replication to disseminate them afterwards. The number of copies to be sprayed is termed as the message quota and represented by \(L\), which after exhaustion, switches to single-copy replication, e.g., Spray and Focus [43] (SnF). In this case, the source node may either utilize the quota alone (i.e., source spraying [44; 45]) or may distribute it to relay nodes that also do the same until their quota is exhausted. In the later approach, quota allocation may be binary or based on other allocation rules.  
In binary quota allocation [44; 45], the relay node is allocated \(1/2\) of the remaining message quota--practically, the relay node gets \(L/2\), and the custodian is left with \(L/2\). The message quota can be allocated based on other rules besides binary allocation. The quasi spray and search algorithm, SAS [46], allocates quota according to a function of the forwarding abilities of the encountered nodes. In ISW [47], the allocation ratio is decoupled from a neighbourhood index that indicates encounter duration with the destination. Other strategies allocate quota according to the ratio of utilities such as: degree centrality [48]; average waiting time between successive encounters [49]; probability of encountering the destination in the near future [50]; frequency of encounters [51]; or one derived from two or more features--for instance, frequency of encounters and encounters with the destination [52].
## 3 Fast-MPC Algorithm Establishment
### LCP Solver  
We transform the constrained quadratic optimization problem of MPC into an equivalent linear complementarity problem. The specific solution steps and proof process of LCPs can be found in the literature [31]. We define the following:  
\[\begin{split}\text{M}&=\widetilde{B}H^{-1}\widetilde{B}^{T }\\ \Gamma&=X^{ul}-\bar{A}\bar{x}(k)+\widetilde{B}H^{-1}F\bar{x}(k) \end{split} \tag{21}\]  
where \(H\) is nonsingular. Meanwhile, M is symmetric and either positive or semipositive definite, based on constraints and \(\widetilde{B}\) row rank. Denoting the Lagrange multiplier vector as Y and non-negative slack variable vector as \(Z\), the QP problem's first-order optimality condition is represented as an LCP:  
\[\begin{array}{ll}\text{Find}&\text{Y},Z\\ \text{such that}&Z=\text{MY}+\Gamma,\\ &\text{Y}^{T}Z=0,\quad Z\geq 0,\quad\text{Y}\geq 0.\end{array} \tag{22}\]  
As the LCP is the dual of the corresponding QP problem, its computational load depends only on the number of inequality constraints. It is important to note that converting to an LCP can, in the worst case, double the number of optimization variables. Let Y\({}^{*}\) be the solution of the LCP problem. Then, the optimal solution \(U^{*}(k)\) of the QP problem can be expressed as  
\[U^{*}(k)=-H^{-1}F\bar{x}(k)-H^{-1}\widetilde{B}^{T}\text{Y}^{*} \tag{23}\]Within this section, the LCP associated with matrices M and \(\Gamma\) is denoted as \(\text{LCP}(\text{M},\Gamma)\). Utilizing the LCP methodology outlined earlier to solve the control law in the context of the MPC problem offers a tangible enhancement in the efficiency of QP solution times. The comprehensive approach and feasibility considerations are meticulously expounded in the referenced article [31].
# Preamble-Based Adaptive Channel Estimation for IEEE 802.11p +
## 3 Channel Estimation Schemes  
In this section, we describe the practical channel estimation schemes that can be selectively used by the proposed scheme. These channel estimation schemes are compared with the proposed scheme in Section 5.
# An Evolutionary Algorithm to Optimise a Distributed UAV Swarm Formation System
## 5 Experimental Results
### Optimisation Results  
We have optimised 10 scenarios per case study by using the three proposed algorithms and achieved the results shown in Table 3. It can be seen that RS achieved the poorest results compared with both EAs, as expected, which confirms the necessity of using an intelligent optimisation algorithm. Then, both EAs present competitive results, statistically compatible for three and five UAVs. In the most difficult case, 10 UAVs, EA.dcx has achieved the best results, outperforming the other two algorithms, which could justify the use of the DCX operator when there is a bigger number of parameters to optimise (bigger solution space). All the results have been tested for normality by using the Shapiro-Wilk test. It can be seen that in most of cases the results are not normally distributed. Consequently, we have used a non-parametric test, i.e., Wilcoxon \(p\)-value, to address the statistical validity of the optimisation results. The best median values were achieved by the EA.dcx in two case studies and by EA.ucx in one. Hence, it is not clear at this point whether selecting one or the other was the best option, although the absolute minimum fitness value was always achieved by EA.dcx. The result distribution obtained from the 30 runs performed shows that except for 10 UAVs, the most difficult case study, both EAs' results are similar.  
Figure 9 shows the distribution of the optimisation results in three boxplots, one per case study. Despite outliers, the boxplots are in concordance with the results presented in  
Figure 8: Parameter sampling frequency per case study.  
Table 3, highlighting the high dispersion of the results of RS for 10 UAVs (lack of accuracy). EA.dcx also shows less accuracy than EA.ucx as the interquartile range is a little larger.  
The convergence of the evolutionary algorithms is presented in Figure 10 where the maximum, mean, and minimum fitness values were plotted as curves. At the end of each line, a continue trace represents the LS stage where the extra improvement achieved at the end of the algorithm execution is clearly shown. Using our hybrid approach resulted in achieving better solutions with a minimal effort (evaluations) as the LS only explores the best solution's neighbourhood. The late improvement shown by the plots confirms the utility of our approach to improve the EA's solutions.  
Figure 10: Convergence of the evolutionary algorithms by case study (30 runs each). The dotted lines represent the GA stages, while the continuous segments are the LS stages.  
Figure 9: Boxplots showing the distribution of the optimisation results.
# Molecular Basis of Irisin Regulating the Effects of Exercise on Insulin Resistance
## 4 Signaling Pathways Mediating the Link between Exercise, Irisin, and Insulin Sensitivity
### GLUT4 and the Interaction between Exercise, Irisin, and Insulin Resistance  
The capacity of exercise to increase glucose uptake in skeletal muscles is partly via up-regulating the expression of GLUT4 [80]. After a program of forty-five-minute treadmill exercise (running at 30 m/min with a 15% grade), GLUT4 expression on the myocyte outer membrane in rats increased by 2.5 times compared with the control [81]. The transportation of glucose to pass through the cell membrane needs a specific carrier, glucose transporters (GLUTs) [82]. Among the most significant glucose transporters proteins, GLUT4 has a strong relationship with insulin action [83]. The impaired expression or function of GLUT4 can lead to the occurrence of IR. Studies have demonstrated that the reduction of GLUT4 causes the appearance of hyperglycemia and hyperinsulin in mice [84; 85], and mice lacking the GLUT4 gene result in IR [86].  
Exercise could raise the quantity of GLUT4 in myocytes by affecting irisin production. Rats were given either high-intensity interval training or moderate-intensity continuous training for eight weeks, and the skeletal muscle expression of the FNDC5 gene both in the diabetic group and the healthy group was significantly increased [87]. Meanwhile, the levels of GLUT4 mRNA in their soleus muscle were also markedly elevated, and the moderate-intensity continuous training group showed greater effects than those of the high-intensity interval training group [87]. Moreover, several studies have shown that exercise can significantly promote the levels of PGC-1\(\alpha\) mRNA and FNDC5 mRNA in rats [75; 77], and p38 mRNA and MAPK mRNA levels in skeletal muscle are also significantly enhanced [41]. Irisin can induce the GLUT4 translocation to the myocyte membrane through phosphorylated p38/MAPK, and the uptake of glucose by skeletal muscle is subsequently increased [88]. Moreover, [PERSON] et al. reported that irisin improved the reduction of glucose consumption in insulin-resistant cardiomyocytes and activated the PI3K/AKT pathway increasing insulin action [89]. Therefore, irisin plays a crucial role in modifying the beneficial effects of exercise on IR by promoting the content and trafficking of GLUT4 within myocytes.