esa-sceva/satcom-chunk-collection huggingface dataset:
# Parallel-Based Corpus Annotation for Malay Health Documents
## 5 Discussion (Challenges)
### Polysemous Terms  
In some cases, the challenge lies in what are known as \"multiple translations\" or \"polysemous terms\". This refers to situations where a single word or phrase can hold multiple meanings or translations in another language, particularly within specialized contexts like medicine or technology. For instance, \"shortness of breath\" and \"breathlessness\" are two medical terms that signify \"difficulty breathing\" or \"shortness of breath\" in English. Both terms share the same translation in Malay, which is \"sesak nafas\".  
This can complicate the selection of the appropriate translation, especially when context sensitivity is a requirement. Context plays a vital role in determining the best translation for such polysemous terms, and this challenge increases when the context is intricate or subject to individual interpretation. This is a common issue in machine translation, and solutions usually employ deep learning models that can consider broader contextual information to better understand and determine an accurate translation.  
Additionally, such polysemous terms also pose a significant challenge to the named entity recognition (NER) systems since the same word or phrase might be classified under different categories based on its different meanings. This introduces the necessity for advanced models that can effectively discern the semantic boundaries of such terms within the given context.  
Moreover, this issue further accentuates the importance of domain-specific knowledge. In the example of \"shortness of breath\" and \"breathlessness\", having knowledge about medical terminologies can guide the translation process more accurately. It highlights the requirement for a multidisciplinary approach, incorporating subject matter expertise in conjunction with computational methodologies, to effectively handle multiple translations and polysemous terms [31].  
Lastly, this challenge also calls attention to the value of extensive, high-quality, and well-annotated corpora, like the Malay Health Document Annotated Corpus. They serve as critical resources for training machine translation and NER systems, enabling them to better understand and handle the complexities of multiple translations and polysemous terms.
# Broadcast Ephemeris with Centimetric Accuracy: Test Results for GPS, Galileo, Beidou and Glonass
## 2 Mathematical Model and Results for GPS, Galileo, BeiDou
### Results for Beidou C07
For T (plot **(d)**) pre-fit (dots) refer to the left y-axis and post-fit (diamonds) to the right y-axis. Figure 7: _Cont._  
The overall improvement for [PERSON]'s C07 in doing the fit described by Equation (1) is summarized as follows: the mean and rms of the pre-fit residuals is \(-6.753\pm 13.310\) m, and \(0.000\pm 0.029\) m for the post-fit residuals. This statistic includes the spatial and temporal coordinates. The large bias in the pre-fit residuals is primarily due to the clock offset of the broadcast message relative to the SP3 values, equivalent to nearly 30 m or \(10^{-7}\) s (Figure 7a). The rms of the pre-fit residuals is \(-6.753\pm 13.310\) m, and \(0.000\pm 0.029\) m for the post-fit residuals. This statistic includes the spatial and temporal coordinates. The large bias in the pre-fit residuals is primarily due to the clock offset of the broadcast message relative to the SP3 values, equivalent to nearly 30 m or \(10^{-7}\) s (Figure 7a)  
the post-fit residuals is dominated by the spatial component: the clock polynomials fit with a typical rms of 0.002 m, about a factor of eight smaller than the rms of the spatial components.
## 3 Experiments and Analysis
### Comparison of Performance Analysis Results of Various Algorithms
In order to evaluate the impact of the improved algorithm in this paper on the overall model performance, the COCO_2017 dataset was selected to conduct a comparative experiment before and after the algorithm improvement. The experiment was used to verify the detection ability of the model for targets of various sizes. The experimental results are shown in Table 4, indicating that the improved model still has good detection performance for targets of various sizes. The detection effects before and after the algorithm improvement are shown in Figure 15. A validation set of six defects was selected and tested on the optimized algorithm. From Figure 15a, it can be observed that the pre-improved model missed some defects on the PCB and had a lower detection accuracy for defects such as mouse_bite, spur, open_circuit, etc. In contrast, Figure 15b demonstrates that the improved model was able to better detect the location of defects, achieving a detection accuracy ranging from 90% to 100% for all six types of defects. In summary, the improved model is proven to be effective. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \begin{tabular}{c} **Size** \\ **(Pixels)** \\ \end{tabular} & \
\begin{tabular}{c} **mAPval** \\ **50–95** \\ \end{tabular} &
\begin{tabular}{c} **FLOPs (\(\times\)10\({}^{9}\))** \\ \end{tabular} \\ \hline YOLOv8n & 640 & 37.3 & 8.7 \\ Improved\_YOLOv8n & 640 & 37.28 & 6.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: COCO_2017 dataset. Figure 15: Comparison of detection results before and after algorithm improvement.
# Fast Reroute Mechanism for Satellite Networks Based on Segment Routing and Dual Timers Switching
## 5 Conclusions  
This paper presents a segment routing-based, dual-timer-controlled fast rerouting scheme aimed at significantly enhancing the communication stability and failure recovery efficiency of satellite networks under link failure scenarios. By integrating a joint fault detection mechanism and optimizing the backup path design, the proposed approach effectively mitigates the impact of both transient disruptions and permanent faults on  
Figure 10: Routing interruption time results for fault scenario A under 60,000 s.  
the path switching overhead. Leveraging collaborative hardware and link fault detection, along with activation and deactivation timers for backup paths, SRDT-FRR demonstrates superior performance in terms of the interruption time and recovery efficiency compared to conventional methods. The simulation results validate its effectiveness, providing strong technical support for the efficient operation of satellite networks. However, the current study assumes single-link failures, which may not fully reflect the nature of real-world LEO constellations. Future work will extend SRDT-FRR to multi-link failure scenarios. Additionally, we plan to deploy a prototype on satellite platforms to evaluate energy consumption and latency under hardware constraints, bridging the gap between simulation and practical deployment. Future research will focus on extending the applicability of SRDT-FRR to more complex fault scenarios and dynamic multi-link changes like node faults, as well as integrating AI techniques to optimize resource utilization and path planning, paving the way for intelligent solutions in integrated space-air-ground-sea networks.  
Conceptualization, J.D. and R.Z.; methodology, J.D.; software, J.H. and R.Z.; validation, J.D., R.Z. and J.H.; formal analysis, T.X.; investigation, J.D.; resources, J.D.; data curation, R.Z.; writing--original draft preparation, J.L. and R.Z.; writing--review and editing, J.L.; visualization, J.L. and R.Z.; supervision, J.D. and T.X.; project administration, J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Young Scientists Fund of National Natural Science Foundation of China (Grant NO. 62201077).  
Not applicable.  
The data generated during this current study are available from the authors upon reasonable request.  
Author [PERSON] was employed by the company China United Network Communication Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Results
### Experimental Point Phantom  
Figure 4 shows the experimental point phantom images. The three point targets indicated by cyan boxes in Figure 4a were used to calculate the FWHM. We can see that DMAS slightly narrows the point width compared to DAS and generates a darkened speckle pattern. The SLSC image shows a poor resolution and dark-region artifacts beside the point targets. CMSB-based images show visually narrowed points in comparison with the DAS image. The LOCw-CMSB (\(\delta=\delta_{ac}\)) image shows a smoothed speckle pattern and improved overall speckle brightness compared with CMSB images. It is noted that the artifacts under the point at a 9.6 mm depth are amplified in LOCw-CMSB and LOCw-CMSB (\(\delta=\delta_{ac}\)) images.  
Figure 5 plots the lateral variations through three point targets at depths of 9.6 mm, 28.1 mm, and 47 mm indicated by cyan boxes, and Table 3 lists FWHM values at different depths. Note that the resolution of the SLSC is not calculated because the image is not  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **FWHM (mm)** & **CR (dB)** & **gCNR** & **sSNR** \\ \hline DAS & 0.55 & \(-\)36.2 & 0.86 & 1.92 \\ DMAS & 0.44 & \(-\)55.3 & 0.62 & 1.34 \\ SLSC & - & \(-\)27.1 & 0.98 & 3.35 \\ CMSB (\(\delta=0.7\)) & 0.29 & \(-\)74.6 & 0.92 & 1.69 \\ CMSB (\(\delta=1.3\)) & 0.26 & \(-\)68.8 & 0.94 & 1.75 \\ LOCw-CMSB & 0.26 & \(-\)78.9 & 0.93 & 1.71 \\ CMSB (\(\delta=\delta_{ac}\)) & 0.26 & \(-\)67.4 & 0.98 & 2.46 \\ LOCw-CMSB & 0.26 & \(-\)77.3 & 0.98 & 2.39 \\ (\(\delta=\delta_{ac}\)) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Image quality metrics in terms of full-width and half-maximum (FWHM), contrast ratio (CR), generalized contrast-to-noise ratio (gCNR), and speckle signal-to-noise ratio (sSNR) of simulated tissue-mimicking images formed using different methods.  
Figure 4: Experimental point images reconstructed by (**a**) DAS, (**b**) DMAS, (**c**) SLSC, (**d**) CMSB (\(\delta=0.7\)), (**e**) CMSB (\(\delta=1.3\)), (**f**) LOCw-CMSB, (**g**) CMSB (\(\delta=\delta_{ac}\)), and (**h**) LOCw-CMSB (\(\delta=\delta_{ac}\)).  
log-compressed. We can see that DMAS narrows the mainlobe over DAS at different depths, resulting in an average resolution improvement of 20.2%. CMSB methods obtain an improved resolution over DMAS at depths of 9.6 mm and 28.1 mm, but a worse resolution at the depth of 47 mm. Specifically, CMSB (\(\delta\) = 0.7) and CMSB (\(\delta\) = 1.3) have average resolution improvements of 16.4% and 26.9% over DMAS, respectively. In addition, LOCw-CMSB preserves the resolution performance of CMSB at different depths. Although CMSB (\(\delta\) = \(\delta_{ac}\)) and LOCw-CMSB (\(\delta\) = \(\delta_{ac}\)) preserve the resolution of CMSB (\(\delta\) = 1.3) at depths of 9.6 mm and 28.1 mm, the obtained FWHM at the depth of 47 mm decreases by 14.3%.
## 2 Methods
### Dissimilarity Representation
*/  Input: minClassLabel, majClassLabel,reProcess,fractionalDistance /* dissimilarityTrainFile and dissimilarityTestFile are filenames used to write the dataset after the dissimilarity transformation. */  Output: dissimilarityTrainFile, dissimilarityTestFile /* Read train and test data. */
1 trainData - spark.read.libsvm(trainDataFile).repartition():
2 testData - spark.read.libsvm(testDataFile).repartition():
3 /* Get R samples from each class. */
4 rMinSamples - trainData.filter(label = minClassLabel).orderBy(rand()).limit(reProcess):
5 /* MinSamples - trainData.filter(label = minClassLabel).orderBy(rand()).limit(reProcess);
6 /* Join R samples and broadcast rSamples. call nodes. */
7 sSamples - rMinSamples.union(rMinSamples);
8 /* Transform feature space into dissimilarity space on train and test data by using the dissimilarityTransform procedure. */
9 trainDSsimData - trainData.map(row -> dissimilarityTransform(row, fractionalDistance));
10 testDSsimData - testData.map(row -> dissimilarityTransform(row, fractionalDistance));
11 /* Save the transformed data into files. */
12 trainDSsimData.write(dissimilarityTrainFile);
13 testDSsimData.write(dissimilarityTestFile);
14
15 Procedure dissimilarityTransform(dataSample, fractionalDistance):
16 /* Initialize classBinSample and classLabel variables. */
17  dissimSample - null;
18  classLabel - dataSample.classLabel. */
19 /* Loop through all elements of rSamples. */
20  fori \(<\) 0 to \(\sigma\)Samples.size do
21 /* Initialized sum variable. */
22  sum = 0; /* Loop through all features of dataSample. */
23  forj \(\sim\) 0 to \(\sigma\)Samples.size do
24 /* Compute the distance between the dataSample and rSample. */
25  sum = + scaling.math.pow((dataSample||) - \(\sigma\)Samples[ij]).abs, fractionalDistance);
26 /* Add the new sample in the dissimilarity space into dissimilarSample. */
27  dissimSample.add(distance);
28 /* Return the class label and the sample data in the dissimilarity space. */
29 return(classLabel/dissimSample)
```  
**Algorithm 1**Dissimilarity Representation in Spark
## 1 Introduction
### Modelling Climate Scenarios with ARPEGE-Climat
#### 2.2.4 ARPEGE-Climat Model Description and Parameterization  
The numerical model ARPEGE is a global and spectral general circulation model developed for an \"operational numerical weather forecast\" by Meteo-France in collaboration with the ECMWF (European Centre for Medium-Range Weather Forecasts). ARPEGE-Climat became the atmospheric part of the CNRM earth-modelling system, which couples different components of the climate system (atmosphere, ocean, land surface, sea ice). The ARPEGE grid can be tilted and stretched by changing the position of the pole and by increasing the horizontal resolution over an area of interest. This zoom ability allows regional climate to be studied with ARPEGE-Climat.  
In our case, ARPEGE-Climat had the pole in Germany (9.97\({}^{\circ}\) E, 50.00\({}^{\circ}\) N). The spatial resolution over Europe was about 20 km. The time step of the model was 600 s (10 min).  
The exchanges between atmosphere and soil were taken into account by the specific SVAT (Soil Vegetation Atmosphere Transfer) module SURFEX (V7) implemented in ARPEGE-Climat.  
The climate forcing allowed the climate to be kept stationary using fixed parameters:  
* Fixed greenhouse gases concentrations accord with the choice of Representative Concentration Pathway (RCP) for the fixed year (here, 2000 or 2050);
* Stationary sea-surface temperature series (adapted to each RCP with a quantile mapping method);
* Fixed stratospheric ozone concentrations;
* Fixed aerosol concentrations.
## 4 Results of Experiments
### GA-RRHC Parameter Tuning  
The GA-RRHC has nine parameters that control its operation. The total number of iterations is \(G_{n}\); \(G_{b}\) is the limit of stagnation iterations, \(S_{n}\) is the number of smart-cells, \(E_{p}\) is the proportion of elite smart-cells, and \(l\) is the number of neighbors of each smart-cell generated by the genetic operators. Further, \(\alpha_{m}\) is the probability of mutation of a solution, \(H_{n}\) is the number of RRHC iterations, \(H_{r}\) are the iterations to restart the RRHC, and \(\alpha_{c}\) is the probability of moving a critical operation in the RRHC.  
For the first three parameters (\(G_{n}=250,G_{b}=50,S_{n}=100\)), the values used in [19; 38] were employed as a reference since these works obtained good results in minimizing the makespan. These parameter values are typically used in specialized publications and are comparable to those utilized by methods employed in the following sections to compare the performance of the GA-RRHC.  
To adjust the other parameters, different levels of each parameter were taken to minimize the \(mt20\) instance of the Vdata dataset and the best combination of parameters was chosen.  
For \(E_{p}\), the values 0.02 and 0.04 were tested, the parameter \(l\) was tested between 2 and 3, and the values 0.1 and 0.2 were tested for \(\alpha_{m}\). For the RRHC, the values between 80 and 100 and between 30 and 40 were taken to tune \(H_{n}\) and \(H_{r}\) respectively. For \(\alpha_{c}\), the values 0.025 and 0.05 were proved. In this way, 64 different combinations of parameters were taken; for each combination, 30 independent runs were performed, selecting the set of parameters with the least average makespan. Table 2 shows the GA-RRHC parameters to analyze its results in the rest of the instances.
# A Machine Learning Approach to Algorithm Selection for Exact Computation of Treewidth
## 4 Experiments and Results  
### Datasets  
The dataset we used is composed of a multitude of publicly available graph datasets--this section will provide a list of all datasets used and where they can be found. Some needed to be converted to the PACE treewidth format from their original format--those are marked with an asterisk (*) in the list below. Additionally, the list will also provide a shorthand name for every dataset. Datasets used are:  
* PACE 2017 treewidth exact competition instances, referred to as **ex**. Available at github.com/PACE-challenge/Treewidth-PACE-2017-instances
* PACE 2017 bonus instances, referred to as **bonus**. Available at github.com/PACE-challenge/Treewidth-PACE-2017-bonus-instances
* Named graphs, referred to as **named**. (These are graphs with special names, originally extracted from the SAGE graphs database). Available at github.com/freetdi/named-graphs
* Control flow graphs, referred to as **cfg**. Available at github.com/freetdi/CFGs
* PACE 2017 treewidth heuristic competition instances, referred to as **he**. Available at github.com/PACE-challenge/Treewidth-PACE-2017-instances
* UAI 2014 Probabilistic Inference Competition instances, referred to as **uai**. Available at github.com/PACE-challenge/UAI-2014-competition-graphs
* SAT competition graphs, referred to as **sat_sr15**. Available at people.mnci.uni-saarland.de/~hdell/pace17/SAT-competition-gaifman.tar
* Transit graphs, referred to as **transit**. Available at github.com/daajoe/transit_graphs
* TreeDecomposition.com database [10]\({}^{\star}\), referred to as **toto**. Available at treedecompositions.com/* PACE 2016 treewidth instances [15], referred to as **pace2016**. Available at bit.ly/pace16-tw-instances-20160307
* [PERSON] and [PERSON] [47]*, referred to as **vc**. Available at ru.is/kennarar/eyjo/vertexcover.html
* PACE 2019 Vertex Cover challenge instances*, referred to as **vcPACE**. Available at paecchallenge.org/files/pace2019-vc-exact-public-v2.tar.bz2
* DIMACS Maximum Clique benchmark instances*, referred to as **dimacsMC**. Available at iridia.ulb.ac.be/~fmascia/maximum_clique/DIMACS-benchmark In total, 30,340 graphs from these datasets were used.
# A Study on a Mechanism of Lateral Pedestrian-Footbridge Interaction
## 5 Study on Pedestrian-Footbridge Lateral Displacement and Lateral Interaction
The absolute displacement of pedestrians is the sum of the pedestrians' relative displacement and the footbridge structure displacement, which results in an absolute displacement of pedestrians of:  
\[ \begin{array}{l}u_{pa}=U_{s}+u_{p}=a_{s}\cos(\omega_{s}t)+a_{p}\cos(\omega_{p} t+\theta_{p})\\ =a_{s}\cos(\omega_{s}t)+a_{p}\cos(\omega_{s}t-\varepsilon\sigma t+\theta_{p})\\ =\sqrt{\left(a_{s}+a_{p}\cos\varphi\right)^{2}+\left(a_{p}\sin\varphi\right)^{ 2}\cos(\omega_{s}t-\phi)}\end{array} . \tag{56}\]  
Therefore, the absolute displacement amplitude is defined as:  
\[A=\sqrt{\left(a_{s}+a_{p}\cos\varphi\right)^{2}+\left(a_{p}\sin\varphi\right)^{ 2}} \tag{57}\]  
After considering \(\cos\varphi=-\frac{2\mu_{p}\omega_{p}\sigma}{a_{s}a_{s}^{2}}\) and then \(\sin\varphi=\pm\frac{\sqrt{\mu_{s}^{2}\omega_{s}^{4}-4a_{p}^{2}\omega_{p}^{2} \sigma^{2}}}{a_{s}a_{s}^{2}}\), we can differentiate the Equation in (58):  
\[A=\frac{a_{p}\sqrt{\left(\eta\omega_{p}-\frac{1}{4}\xi\omega_{p}a_{p}^{2}- \frac{1}{4}\rho\omega_{p}^{3}a_{p}^{2}\right)^{2}+4\omega_{p}^{2}\sigma^{2}-4 \omega_{s}^{2}\omega_{p}\sigma+\omega_{s}^{2}}}{\omega_{s}^{2}}. \tag{58}\]  
The Phase Angle between the absolute displacement of pedestrians and the footbridge displacement is expressed in Equation (59):  
\[\tan\psi=\frac{a_{p}\sin\varphi}{a_{s}+a_{p}\cos\varphi}. \tag{59}\]  
Equations (37), (54), and (55) are inserted into Equation (59). This leads to:  
\[\tan\psi=\frac{(\eta-\frac{1}{4}\xi a_{p}^{2}-\frac{1}{4}\rho\omega_{p}^{2}a_{ p}^{2})\omega_{s}^{2}}{\omega_{p}(\eta-\frac{1}{4}\xi a_{p}^{2}-\frac{1}{4} \rho\omega_{p}^{2}a_{p}^{2})^{2}+4\omega_{p}\sigma^{2}-2\omega_{s}^{2}\sigma}. \tag{60}\]  
From Equation (58), it can be known that the absolute displacement amplitude of pedestrians is related to the following parameters: lateral response amplitude of the footbridge, relative displacement of pedestrians, Phase Angle, frequency difference, and the time of footbridge displacement, which in turn has nothing to do with the mass of the footbridge and pedestrians. Consider that the parameters are constant, including the lateral response amplitude of the footbridge, the relative displacement of pedestrians and the Phase Angle, frequency difference, and time of footbridge displacement. In this case, the absolute displacement amplitude of pedestrians is linear with respect to the response amplitude. In addition, the larger the amplitude of the footbridge response is, the larger the absolute displacement amplitude of pedestrians becomes. When \(\phi<0\), the absolute displacement of pedestrians lags behind the displacement of the footbridge. Conversely, in the case of \(\phi>0\), the absolute displacement of pedestrians is ahead of the footbridge displacement.
## 3 Results  
In total, 223 articles were identified (Figure 1). After 73 duplicates were removed, the remaining 150 articles underwent the first selection based on inclusion/exclusion criteria and title/abstract. After screening for relevance, 128 articles were excluded, and the remaining 22 papers underwent full-text assessment for eligibility. At the end of this selection, 14 original papers were included for fulfilling the requirements and were analyzed for qualitative review (Table 2).
###### Abstract  
Background/Objectives: Enhancer RNAs (eRNAs) function in diverse modes and increasing studies have shown that they play important roles in normal development and disease. However, their role in erythropoiesis is not fully understood. **Methods**: We analyzed published RNA-seq and Promoter Capture Hi-C data from mouse E14.5 fetal liver cells to identify enhancer RNAs in erythroid cells with long-range interactions. **Results**: We discovered an erythroid-specific enhancer RNA (_CpoxeRNA_) transcribed from an enhancer region upstream of _Cpox_, an enzyme important for heme synthesis. _CpoxeRNA_ is important for erythropoiesis, as the knockdown of _CpoxeRNA_ by shRNA results in impaired enucleation and cell proliferation during terminal differentiation. _CpoxeRNA_ interacts with cohesin and acts both _in cis_ and _trans_ to regulate erythroid genes. **Conclusions**: we have identified a _trans-acting_ eRNA, _CpoxeRNA_, as a potential regulator of terminal erythropoiesis.  
eRNA; erythropoiesis; trans acting; CTCF/cohesin 2025