esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analyses
### Ablation Study  
#### 4.5.1 Effect of Model Hyperparameters  
In order to explore the effect of the kernel size in CBHNN and the number of cross-modal attention layers in CMTE on the performance of CGR-NER, we select different parameter values and evaluate them. The experimental results are shown in Tables 7 and 8.  
Table 7 shows the impact of different kernel sizes on model performance. It can be seen that three different kernel sizes have achieved better performance than BERT-BiLSTM-CRF, indicating that different kernel sizes have positive effects on model performance. However, the F1 score does not increase as the kernel size increases. One possible reason is that the small kernel size is more focused on extracting local details, so the glyph features have richer semantic information.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1 Score** \\ \hline SDI-NER [21] & - & - & 68.28 \\ FLAT+BERT [22] & - & - & 68.55 \\ AESINER [23] & - & - & 69.78 \\ MECT [29] & - & - & 70.43 \\ MW-NER [27] & - & - & 69.41 \\ LEBERT [26] & - & - & 70.75 \\ KGNER [25] & - & - & 71.90 \\ StyleBERT [31] & - & - & 69.60 \\ GlyNN [33] & - & - & 69.20 \\ PLTE+BERT [24] & 72.00 & 66.67 & 69.23 \\ MFE-NER [30] & 70.36 & 65.31 & 67.74 \\ Glyce [32] & 67.68 & 67.71 & 67.60 \\ ChineseBERT [34] & 68.27 & 69.78 & 69.02 \\ BERT-BiLSTM-CRF & 69.86 & 67.20 & 68.40 \\ CGR-NER & 70.23 & 71.17 & **70.70** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results on Weibo dataset.  
It can be seen from Table 8 that the three different layer sizes have achieved better performance than BERT-BiLSTM-CRF. When the number of layers is set to 2, the F1 score on the two datasets reaches the best value. When the number of layers increases to 3, the performance of CGR-NER does not continue to be improved. One possible reason is that the excessive number of layers may cause the model to over-fit or even learn noise, resulting in reduced recognition performance.
###### Abstract  
Strain gauges are commonly used for tension tests to obtain the strain of a metal test specimen. They make contact, however, so the gauges are not applicable to every type of test specimen. That is the reason why a non-contact type measurement system is required. Nanofibrous mats, manufactured by electrospinning, have different structures and thicknesses. Displacement and strain distributions for all ranges of the specimen have never been demonstrated for nanofiber mats so far. Wrinkled nanofibrous mats of polyurethane were made and then tension-tested. The Digital Image Correlation (DIC) method was employed to measure displacement, then to calculate strain for all areas of the specimen. The DIC system consisted of a CMOS camera, control PC and operating software with a DIC algorithm: then, the Center of Gravity (COG) algorithm was used for this system. A cross-head speed of 3 mm/min was set for the tension test. The image record speed was one frame a second. In total, 400 image frames were obtained from the start, and then displacement and strain distributions were acquired for a 400 second tension test. The strain distribution from DIC system showed good agreement with the test result by a universal testing machine.  
digital image correlation; nanofiber mat; tension test; strain distribution +
Footnote †: journal: Applied Sciences
## 6 Conclusions  
This paper presents a novel approach to water level measurement using a combination of improved SegFormer-UNet and a virtual water gauge, which addresses the limitations of previous computer vision-based methods that rely heavily on gauge information. Specifically, we transform the water level measurement problem into a water body segmentation task by establishing a mapping relationship between the water gauge and the image. Our proposed SegFormer-UNet model achieves an impressive average pixel accuracy of 99.10% and an Intersection Over Union of 98.34% on the segmentation index, outperforming the original SegFormer model and other mainstream segmentation networks. Additionally, we demonstrate that our method accurately detects the water level line with an average absolute pixel error of 1.73 pixels compared to the actual water level line. In field tests, our method achieves water level measurements with an average absolute deviation of less than 1 cm, meeting the requirements for practical applications. Notably, we show that our method can measure water levels without relying on a physical gauge while maintaining the same camera imaging angle. To enhance the generalizability of the algorithm, future work will focus on improving the model's generalization capability for different water surface environments, streamlining the model structure for faster inference, and increasing the diversity of the dataset for a thorough validation of the algorithm's generalizability.  
Conceptualization, Z.X., J.J. and J.W.; methodology, J.J.; software, Z.X.; validation, Z.X., J.J., R.Z. and S.L.; formal analysis, J.J.; investigation, J.J.; resources, J.J. and J.W.; writing--original draft preparation, Z.X.; writing--review and editing, J.J.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript. This research was supported by \"Yunnan Xingdian Talents Support Plan\" project of Yunnan and Key Projects of Yunnan Basic Research Plan (202101 AS070016).  
Not applicable.  
The authors declare no conflicts of interest.
## 4 Results
### Correlation Analysis of Organic Matter Content with Different Spectral Transformation Forms  
As described in Section 2.2, the soil spectral data in this study underwent several preprocessing steps. Firstly, the original spectral curves were subjected to SG smoothing. Then, several spectral transformations, including FD, SD, RFD, RSD, LRSD, LRSD, RLFD, and RLSD, were used. Figure 7 displays the visible outcomes of these modifications.  
To further investigate the correlation between the transformed spectral reflectance and the measured SOM content, an analysis was conducted on the results of SG smoothing and the eight transformations for the first sample. The visual results are presented in Figure 8. In Figure 8, it can be observed that the original spectral curve, N, exhibited a negative correlation with the organic matter content, and the correlation coefficient curve was relatively smooth. By combining Figure 8 and Table 3, it is evident that the FD transformation yielded  
Figure 6: Original spectrum and curve of soil. (Different colored lines represent different samples).  
Figure 7: SG smoothing and 8 spectral transformations. (Different colored lines represent different samples).  
the highest correlation coefficient value, with an absolute value of 0.55. Additionally, all of the differential transformation curves exhibited significant fluctuations around 1400 nm, indicating the presence of minor absorption valleys influencing the spectral absorption in that region. The correlation coefficients between the SOM and the original spectra after being transformed by RFD, LRFD, and RLFD exhibited a negative correlation. The absolute values of their maximums are 0.52, 0.51, and 0.53, respectively, with significant fluctuations in the curve transformations. On the other hand, the shapes of the curves are relatively similar after the transformations via SD, LRSD, and RLSD. The correlation coefficients for these transformations are all in the range of \(-0.4\) to \(0.4\), indicating a weak correlation between the transformed values and SOM. On the other hand, the RSD transformation showed positive correlation coefficients, indicating a positive relationship with the SOM. The curve fluctuations for RSD transformation are relatively small. The different forms of differential transformation significantly improved the correlation between the soil spectral reflectance and the SOM content. This shows that the spectral data processed using the differential transformation method can be effectively used for modeling the SOM content.
# Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai
## Evaluation  
### Datasets  
We use the real-world downstream traffic volume data in two campus networks of DHU and ECNU. The time range is from 00:00 CST on 21 July 2021 to 12:00 CST on 23 September 2021. The average traffic volume was recorded once every two hours. In chronological order, we select the traffic data within the first 70% of the time range as training data, the following 20% as validation data, and the last 10% as test data to evaluate the performance.
# A Deadline-Aware Scheduling and Forwarding Scheme in Wireless Sensor Networks
## 6 Performance Study
### Performance Comparison with Other Algorithms
#### 6.2.3 Effects of the Number of Sources  
In this subsection, we can see how network performance is achieved when passing the number of nodes that generate the data packet whenever an event occurs from one to 16 sources. The duty cycle interval is calculated as 18.51 s using Equation (17) according to the variety of network parameters,such as the number of nodes, node density and delay requirement. Figure 8a shows that when the number of sources increases, PDR tends to decrease. However, similar to Subsection 6.2.2, LPF shows the strongest drop in PDR among the three considered algorithms. For example, when the number of source nodes is 16, PDR is 96.6%, 98.0% and 99.3% for LPF, DASF and RAW algorithms, respectively.  
In terms of average E2E delay, Figure 8b shows that the average E2E delay in the three algorithms stays relatively flat, but DASF can achieve the lowest packet latency among the three considered algorithms. This is attributed to the fact that the duty cycle interval, not the number of sources, plays an important role in the average E2E delay. As a result, packet latency remains relatively consistent with the different number of sources. For instance, when there are eight source nodes, the average  
Figure 7: Effects of the event rate. (**a**) effects on packet delivery ratio, (**b**) effects on average E2E delay, (**c**) effects on collected DCSR.  
E2E delay is around 9.76 s under our algorithm compared to approximately 12.14 s and 27.40 s under LPF and RAW, respectively.  
Moreover, as can be seen from Figure 8c, our work and LPF can satisfy the delay requirement, whereas RAW does not meet the required DCSR. Note that DASF achieves the highest collected DCSR in all cases. For example, when there are eight source nodes, 96.9% of the packets can be transmitted to the sink within 20 s compared to 95.5% and 27.2% with LPF and RAW, respectively.  
According to these results, we can conclude that the proposed algorithm not only meets the required latency given the delay-constrained success ratio, but also achieves low delay compared to  
Figure 8: Effects of the number of sources. (**a**) effects on packet delivery ratio, (**b**) effects on average E2E delay, (**c**) effects on collected DCSR.  
existing algorithms. In addition, the DASF algorithm can achieve a high packet delivery ratio over different network parameters.
# Autonomous and Earth-Independent Orbit Determination for a Lunar Navigation Satellite System
## 2 Lunar Scenario
### Orbital Design  
Given the non-uniformity of the Moon's mass distribution, and perturbations caused by the strong pull of the Earth and the Sun, most lunar orbits are highly unstable. For a satellite-based infrastructure that can support operations on the Moon, a stable orbit with minimal station-keeping is desirable.  
Lunar orbital regimes for delivering a ranging service have been studied extensively in the literature. NRHO [9; 22], Low Lunar Orbit (LLO) [9], and Direct Rectilinear Orbit (DRO) [23] have all been considered for lunar navigation architectures, with NRHO being of particular interest since it will host the Lunar Gateway. The orbit most frequently proposed, however, is the Elliptical Lunar Frozen Orbit (ELFO), which is both stable and sufficiently close to the surface [4; 5; 6; 17].  
Several attempts have been made to optimise the solution, using parameters such as satellite observability and sky geometry, minimal station-keeping manoeuvres, and cost-effective insertion from Earth trajectory [24; 25]. Most solutions congregate to the ELFO solution proposed by [26].  
The orbits of an ELFO are illustrated in Figure 1 and the parameters are contained in Table 1. The orbital period is approximately 19 h and provides strong coverage of the Lunar poles during the apogee, where the South Pole is chosen. An ELFO can also vary in the right ascension of the ascending node, so satellites can be placed across different planes. For a navigation service in particular, this is highly beneficial to improving visible satellite geometry.  
The number of satellites and plane separation in Figure 1 are just for illustration purposes. Work by [7] has optimised for either nine satellites distributed across three ELFO orbital planes or ten satellites across two planes. In this work, the 10-satellite distribution is used.  
It is important to note that these parameters are established in the Moon's Mean Earth (ME) frame [26; 27], and not in an inertial frame as for Earth-originated Kepler  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Semi-Major Axis & 8049 km \\ Eccentricity & 0.4082 \\ Inclination & 56\({}^{\circ}\) \\ Orbital Period & 19 h \\ Number of Satellites & 9–10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: ELFO orbital parameters.  
Figure 1: Trajectory of the evaluated lunar navigation constellation in ELFO.  
parameters. The position and velocity are solved by the general conversion equations from Kepler to Cartesian [28], and then transformed from the ME to the J2000 frames.
# Low-Cost SDR for GNSS Interference Mitigation Using Spatial Diversity Techniques +
## 3 Exploiting Spatial Diversity for Interference Mitigation  
Given that before the despreading process, the GNSS signal remains buried below the noise floor, there is little to no information that can be acquired from it. In certain cases, one can have information about its DoA, relying on external services that provide the satellite ephemerides and with knowledge of the receiver's orientation or by applying specific techniques for direction finding [14; 15]. However, in the case of GNSS, direction finding entails despreading the signal and working at post-correlation once it is visible, which, as previously stated, is not always possible when an interference is present.  
Considering these limitations and the goal of a low-complexity scheme at the receiver, this work analyses two blind beamforming techniques. The term \"blind\" is used to state that these algorithms do not need any prior knowledge or additional information to compute the weights, and only the incoming samples (\(\mathbf{x}[n]\)) are evaluated by to produce the set of coefficients.
## 2 The Electro-Optical Trap
For positive ions, the electrodes generating the quadrupole potential must be negatively charged. (a magic wavelength for the \(6\,^{2}\)S\({}_{1/2}\leftrightarrow 6\,^{2}\)P\({}_{1/2}\) transition of Ba\({}^{+}\)[17]), \(w_{0x}=w_{0y}=40\,\upmu\)m, and \(2\theta_{c}=10\,^{\circ}\) is presented in Figure 2. The figure shows that the electrode potentials must be negative for ensuring an attractive potential along the y and z axes. Moreover, the optical potential must be deep enough to prevail over the anti-trapping electric potential along the x axis. The red line in Figure 2 corresponds to the parameters \(\{P_{\text{iso}},\phi_{\text{iso}}\}\) for which there is a 3D isotropic confinement. Therefore, for a given laser power \(P\), the shape of the electro-optical potential can be tuned by changing the electric potential \(\phi\) on the electrodes in the range [0, \(\phi_{\text{trap}}\)], where \(\phi_{\text{trap}}\) is the largest value for which the particles are still trapped along the interference direction (x axis). The possible scenarios are thus:  
* \(0>\phi>\phi_{\text{iso}}\): The ions can be trapped in a single minima of the optical lattice, thus forming a disk-shaped crystal. * \(\phi=\phi_{\text{iso}}\): The three trapping frequencies are equal, so the potential is isotropic. * \(\phi_{\text{iso}}>\phi>\phi_{\text{trap}}\): The confinement along the interference direction is weaker than the other two. Considering the typical depth of the optical potentials and the Coulomb repulsion, the ions might lie in different minima of the optical lattice.
# Research on Out-of-Plane Bending Test of PVB Laminated Glass Plate with Different Number of Layers
## 3 Results and Discussion
### The Influence of the Number of Layers of Glass on Ultimate Load  
To evaluate the effect of the number of glass layers on the mechanical response, bending experiments were conducted on one-layer, two-layer, and three-layer glass plates under four-sided simple support conditions. Figure 11 depicts the load-deflection relationship for PVB LG with varying numbers of layers. As illustrated in the figure, the central deflection increases approximately linearly with the increasing load, aligning with the findings of [PERSON] et al. [38].  
Figure 11: Load–central deflection curve of LG of different layers.  
Figure 10: Distribution of maximum principal stress on the surface of 3L-a.  
Figure 12a presents a comparison of the ultimate load-bearing capacities of PVB LG with different layer counts. The data indicate that the average ultimate load-bearing capacities for three-layer, two-layer, and single-layer glass plates are 37.8 kN, 36 kN, and 24.7 kN, respectively, corresponding to a ratio of 1:0.95:0.65. The ultimate load capacity of the two-layer PVB LG is significantly enhanced compared to that of the single-layer glass plate, showing an increase of 45.7%. However, the three-layer PVB LG exhibits only a 5% increase in ultimate load capacity relative to the two-layer PVB LG, despite a 50% increase in thickness. Evidently, from an ultimate load-bearing capacity perspective, utilizing two-layer PVB LG is more cost-effective than three-layer PVB LG in construction projects.  
By dividing the ultimate load-bearing capacity of PVB LG (_Nu_) by the total thickness of the glass (_t_), the ultimate load capacity per unit thickness is obtained. Figure 13 shows the relationship between ultimate load capacity per unit thickness and the number of glass layers (n). It is evident from the figure that a smaller number of layers results in a higher ultimate load capacity per unit thickness. This finding is in agreement with the theoretical research of [PERSON] [39]. Therefore, unless specific requirements dictate otherwise, it is recommended to select two-layer PVB LG for economic considerations.
## 2 Sparse Direct Algorithms  
Sparse direct methods solve systems of linear equations by factorizing the matrix \(A\), generally employing graph models to limit both the storage needed and work performed. Sparse direct solvers have a number of distinct phases. Although the exact subdivision depends on the algorithm and software being used, a common subdivision is as follows.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Package** & **Year** & \
\begin{tabular}{c} **Real/** \\ **Complex** \\ \end{tabular} &
\begin{tabular}{c} **Positive** \\ **definite** \\ \end{tabular} & **Indefinite** & **Out-of-core** & **Bit-compatible** & **Notes on parallelism** \\ \hline HSL\_MA77 & 2006 & Real & ✓ & ✓ & ✓ & ✓ & Node-level parallelism only. \\ HSL\_MA86 & 2011 & Both & & ✓ & & & Fast task-based parallelism. \\ HSL\_MA87 & 2009 & Both & ✓ & & & & Fast task-based parallelism. \\ HSL\_MA97 & 2011 & Both & ✓ & ✓ & & ✓ & Constrained tree and \\  & & & & & & node-level parallelism. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key features of the new HSL sparse direct solvers. Here, the year is when the solver was first included within HSL.  
1. An ordering phase that exploits the sparsity (non-zero) structure of \(A\) to determine a pivot sequence (that is, the order in which the Gaussian elimination operations will be performed). The choice of pivot sequence significantly influences both memory requirements and the number of floating-point operations required to carry out the factorization.
2. An analyse phase that uses the pivot sequence to establish the work flow and data structures for the factorization. This phase generally works only with the sparsity pattern of \(A\) (this is the case for all the solvers in this study).
3. A factorization phase that performs the numerical factorization. Following the analyse phase, more than one matrix with the same sparsity pattern may be factorized.
4. A solve phase that performs forward elimination followed by back substitution using the stored factors. The solvers in this study all allow the solve phase to solve for a single right-hand side or for multiple right-hand sides on one call. Repeated calls to the solve phase may follow the factorization phase. This is typically used to implement iterative refinement (see, for example, [9]) to improve the accuracy of the computed solution.
## 2 Ad hoc On-Demand Distance Vector Routing and Hotline Based Routing  
AODV is a loop free single path reactive routing protocol for ad hoc networks. It is based on concepts of on-demand route discovery and destination sequence number from DSR [3] and DSDV [4]. AODV differs from DSR due to its hop by hop routing approach nature instead of source node routing. The following are some features of AODV that are required by WEAMR for its implementation.
# Detection of RINEX-2 Files With Mixed GPS L2P(Y)/L2C Carrier Phase Observations
## 3 Detection Algorithms
### Algorithm 3: Comparison with RINEX-3 Observation File  
In the cases where RINEX-2 and corresponding RINEX-3 observation files are both available, one-to-one comparison of measurements reveals whether the RINEX-2 file contains mixed L2 phase observations. In addition, comparing simultaneous RINEX-3 carrier phase measurements on the same frequency reveals whether phase alignment was performed.  
In the example of Table 1, the RINEX-2 file contains GPS carrier phase observations on three frequencies: L1, L2, and L5. The corresponding RINEX-3 file lists four different GPS phase observations: L1C, L2S, L2W, and L5Q. On GPS frequencies one and five, the numerical values are identical or differ by 0.001 cy maximum. Any matching of observation codes is trivial: L1-L1C, L5-L5Q. On the second frequency, the RINEX-3 file contains phase observations obtained in two different tracking modes: L2W (L2P(Y)) and L2S (L2C, CM code). L2W observations are available for all satellites, L2S for BlockIIR-M and IIF only. When L2W and L2S are both available, their numerical values differ by a few full cycles and some hundredth of a cycle, but not by a quarter cycle. Thus, these two observations are aligned in RINEX-3, which is also indicated by the \"PHASE SHIFT\" information in the header section of the file. For non-L2C satellites, L2 observation values are identical to L2W, or they differ by 0.001 cy maximum. For all other satellites, full cycles of L2 are identical to L2S, but not to L2W. However, there is a quarter cycle phase shift between L2 and L2S, which means that the alignment of L2S was not performed for L2. The conclusion is that the L2 carrier phase observations are a mix of L2P(Y) and L2C. Since they are not aligned, the L2 quarter cycle problem exists in this RINEX-2 file.