esa-sceva/satcom-chunk-collection huggingface dataset:
# An Emerging Role of Long Noncoding RNAs as Novel Biomarkers for Breast Cancer Metastasis
## 3 Molecular and Functional Insights into Breast Cancer Metastasis
### A Brief Biogenesis of lncRNAs  
LncRNA is a type of noncoding RNA defined as a molecule longer than 200 nucleotides that does not encode proteins. The length of lncRNA is comparable to that of mRNA, although it contains fewer exons. The biogenesis of lncRNA occurs primarily in the cell nucleus, where it is transcribed by RNA polymerase II (Pol II) from genomic DNA. Once transcribed, lncRNAs undergo various post-transcriptional processing steps, including 5\({}^{\prime}\) capping, splicing, and polyadenylation, similar to protein-coding mRNAs. Some lncRNAs exist in the cytoplasm, but they are not translated [13]. A classification of lncRNAs is shown in Figure 1.  
LncRNAs interact with DNA, RNA, or proteins, impacting the structure and function of these molecules. Within the nucleus, they guide chromatin-modifying complexes or transcription factors, while in the cytosol, they regulate mRNA stability or compete for protein expression machinery access. Numerous studies have demonstrated that lncRNA plays a role mainly in the formation, invasion, progression, and metastases of BC, particularly in triple-negative breast cancer (TNBC). Only a small number of lncRNAs are responsible for inhibiting these processes. Furthermore, an increasing number of studies are investigating the relationship between lncRNAs and drug resistance, revealing that lncRNAs play a significant role in carcinogenesis [14].
## 4 Conclusions  
Improving calibration accuracy is an important means to improving the structured light vision measurement model. The paper proposes a line-structured light calibration method with multiple geometric constraints. On the basis of the structured line light vision measurement process, there are errors in the detection of the light stripe center and the solution of the camera coordinate of these points, which cause errors in the data points involved in the calculation. In theory, the data points are on the light plane, and the error points can be eliminated by the geometric constraint. To further optimize the parameters of the light plane equation, the laser was moved along a line, and the laser planes corresponding to each position were parallel to each other. The objective function for the parameter optimization of the light plane equation was established through the multiple parallel relationships and was solved by the nonlinear optimization algorithm. In the experiment, the thickness of the gauge block was obtained from the light plane equation, and the measured thickness of the standard gauge block was used to evaluate the calibration accuracy of the optical plane. The measurement errors of the basic calibration model and the calibration method proposed in this paper were compared, and the number of parallel constraints was discussed. The results of the experiment showed that the measurement error of the method proposed in this paper was significantly smaller than that of the basic measurement model, and when the number of parallel constraints was five, the calibration accuracy was the highest.  
Data curation, S.L.; Writing - original draft, C.L.; Writing - review & editing, Z.R.; Supervision, X.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant 52005213 and the Department of Science and Technology of Jilin Province under Grant 20220201040 GX.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original data can be obtained by contacting the corresponding author.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gauge Block Thickness** & **Method A** & **Method B** & **Method C** \\ \hline
3 & 0.043 & 0.028 & 0.023 \\
4 & 0.040 & 0.020 & 0.027 \\
5 & 0.034 & 0.021 & 0.025 \\
6 & 0.042 & 0.015 & 0.021 \\
8 & 0.039 & 0.025 & 0.023 \\
10 & 0.046 & 0.015 & 0.027 \\ Mean error & 0.041 & 0.021 & 0.024 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of calibration accuracy (mm).
# Semi-Supervised GNSS Scintillations Detection Based on DeepInfomax
## 5 Overview of Functioning and System Description
To be completely precise, as explained in Appendix B, the actual input to the classifier is the concatenation of feature vector \(F\) and the result of skip connections taken from the layers of the first convolutional network \(e^{1}_{\eta}(\cdot)\). This is done only for the time instants for which there is a reference. For all time instants for which a reference signal exists, the corresponding feature \(F_{r}\) is fed to fully connected classifier \(c_{\phi}(\cdot)\) that receives as reference signal \(Y_{r}\), the corresponding label scintillation/no scintillation for time instant \(r\). 2. **local Mutual Information (MI)** computation: starting from the collections of \(\{(H_{r},F_{r})\}\) couples extracted from the joint and marginal distributions are built. These couples are fed to the network \(g^{loc}_{\theta}(\cdot)\) whose output is the input of the \(\mathcal{C}_{MI,local}\) cost function, defined in (A7). 3. **adversarial matching** of features \(F\) distribution to a target uniform distribution. Random variables \(\tilde{F}\sim U(-1,1)\) are generated. A discriminator is fed with both the random variables generated and the \(F\) (the rest of the system is trained adversarially [27] to fool the discriminator). This is used as a regularized to ensure that the features \(F\) cover evenly the latent space. 5. Standard backpropagation is performed, the cost function considered is \(\mathcal{C}=\mathcal{C}_{MI,local}+\mathcal{C}_{adversarial}+\mathcal{C}_{ classification}\), where the subscripts represent the various component of the cost function. During testing, the only portion of the network that is used is the one related to classification, for simplicity depicted in Figure 6 that is clearly much simpler and lighter. In a realistic scenario, the training could be then performed offline on powerful machines while the lighter scintillation detection system could be subsequently uploaded on SDR receivers where the computational capabilities and the maximum power consumption are stricter.
## 3 Results
### Comparison of Independent Detection Accuracy of Traffic Flow Parameters under Noise Interference  
Since the noise had a certain impact on the image, we compared the anti-noise effect of different methods, selected a monitoring video, extracted different noise points in the video as the detection object, marked them as 1-5, and got the results of the independent detection accuracy of traffic flow parameters under different noise interference, as shown in Figure 7.  
Figure 6: Schematic diagram of VANET regional collaborative information transmission.  
According to the analysis of Figure 7, for different noise points, the methods of References [5; 6; 7] and this paper had different accuracy rates of traffic flow parameter autonomous detection. The highest accuracy of the Reference [5] method was 49%, the highest accuracy of the Reference [6] method was 65%, and the highest accuracy of the Reference [7] method was only 15%. In comparison, the highest accuracy of this method was 70%, which was higher than the traditional method. An analysis of the trend of several methods shows that the method in this paper had the largest growth. From the above analysis, it can be seen that the accuracy of traffic flow parameter detection results obtained by this method was higher under different noise interference, which shows that this method can reduce the interference caused by noise and the detection results are more reliable.
###### Abstract  
Chlorophyll fluorescence (CF) has been applied to measure the chlorophyll content of seeds, in order to determine seed maturity, but the high price of equipment limits its wider application. _Astragalus_ seeds were used to explore the applicability of digital image analysis technology to the prediction of seed chlorophylll content and to supply a low cost and alternative method. Our research comprised scanning and extracting the characteristic features of _Astragalus_ seeds, determining the chlorophyll content, and establishing a predictive model of chlorophyll content in _Astragalus_ seeds based on characteristic features. The results showed that the R\({}^{2}\) of the MLR prediction model established with multiple features was \(\geq\)0.947, and the R\({}^{2}\) of the MLP model was \(\geq\)0.943. By sorting of two single features, the R and G values, the R\({}^{2}\) reached 0.969 and 0.965, respectively. A germination result showed that the lower the chlorophyll content, the higher the quality of the seeds. Therefore, we draw a conclusion that digital image analysis technology can be used to predict effectively the chlorophylll content of _Astragalus_ seeds, and provide a reference for the selection of mature and viable _Astragalus_ seeds.  
Astrragalus seeds; chlorophyll content; multiple linear regression (MLR); multilayer perceptron (MLP); R; G
# Utilization of Yacon Damaged Roots as a Source of FOS-Enriched Sweet-Tasting Syrup
## 3 Results and Discussion  
### Yacon Varieties Composition  
The damaged roots of the Hualqui and Crespo yacon varieties were found to have a pH of 6.0, with dry matter contents of 11% and 14%, respectively. The total soluble solids accounted for 6.7 \({}^{\circ}\)Brix for the Hualqui variety and 10.1 \({}^{\circ}\)Brix for the Crespo variety. These features were comparable to the varieties cropped in the Peru region [9; 31], which are reported to have pH levels ranging from 6.0 to 6.5, dry matter contents of 8 to 13%, and total soluble solids of 8 to 11 \({}^{\circ}\)Brix. Cultivars grown in the southwest region of Germany exhibit dry matter contents of 9 to 17% [2; 32]. The proportion of pulp in Hualqui and Crespo-damaged roots was 80%, consistent with the percentages remaining in yacon juice extraction [21]. Accounting for the chromatogram (Figure 2), the overall composition and yields of EtSn and EtRe (Tables 1 and S1), as well as the proportion of skin and pulp, the whole carbohydrate composition of the yacon Hualqui and Crespo varieties were determined. As shown in Figure 3a (values in Table S1), the Hualqui variety exhibited higher concentrations of Glc (73 g/kg dry weight) and Fru (210 g/kg) compared to the Crespo variety (41 g/kg of Glc and 90 g/kg of Fru). When comparing these findings to varieties grown in Southwest Germany [2], the Crespo and Hualqui varieties had nearly 4 to 30 times more Fru. While other factors likely contribute to these differences, variations in the edapholcimatic conditions and genetic factors [2; 33] play a significant role. The Hualqui variety contained a higher proportion of lower molecular weight FOS (Figure 3b), with an average DP of 5, in contrast to the Crespo variety, which had FOS with an average DP of 6. The Crespo variety was found to have a total fructan (FOS + inulin) content of 509 g/kg. These levels are consistent with the values reported for yacon flour (360-670 g/kg) from varieties cultivated in tropical climates of Brazil [20] and the Andean region of Peru (540-620 g/kg) [31]. The fructan content of Crespo-damaged roots is also comparable with highly regarded fructan sources such as artichokes (290 g/kg) [34], Jerusalem artichoke (400-690 g/kg) [35], chicory (450-587 g/kg) [36; 37], asparagus (579 g/kg) [37], and agave (809 g/kg) [37; 38]. In contrast, the damaged roots of the Hualqui varieties herein studied displayed a lower fructan content (150 g/kg). The Hualqui variety also contained lower levels of Ara (3 g/kg) and Gal (2 g/kg) (Table 2), sugars characteristic of the neutral side chains of pectic polysaccharides [9; 22], in contrast to the Crespo variety, which featured 12 and 6 g/kg, respectively. The occurrence of Glc in the Hualqui variety also suggests the oc-curve of about 26 g/kg glucans, including cellulose [9]. Due to the presence of inulin and FOS in the EtRe fractions of the Crespo variety, the determination of glucans was not performed because the methodology used does not distinguish Fru from Man and Glc [39].
## 2 Theoretical Basis  
### Point Safety Factor Based on Mohr-Coulomb Failure Criterion  
When nonlinear deformation begins, the stress at this point is defined as yield strength. Yield criterion is a hypothesis concerning the limit of elasticity under any combination of stresses. The Mohr-Coulomb failure criterion is widely used to describe the response of geotechnical materials to shear stress as well as normal stress [29]. The Mohr-Coulomb yield surface divides the principal stress space into two individual areas, elastic zone and plastic zone. And the Mohr-Coulomb yield function can be expressed by principal stresses as Equation (1):  
\[\mathbf{f}(\sigma)=\frac{1}{2}(\sigma_{1}-\sigma_{3})-\frac{1}{2}(\sigma_{1}+ \sigma_{3})\sin-\epsilon\cos \tag{1}\]  
where \(\mathbf{f}\) is the function of the yield criterion; \(\sigma_{1}\) and \(\sigma_{3}\) are the maximum principal stress and the minimum principal stress, respectively; \(\epsilon\) is the cohesion and \(\phi\) is the angle of internal friction.  
As mentioned above, the point will yield when the stress state at this point exceeds the material strength. To indicate the stability of rock mass, the point safety factor is introduced by an engineer on the basis of elastic theory as Equation (2).  
\[\mathbf{F}=H(\chi)/\mathbf{f}(\sigma) \tag{2}\]where \(\mathbf{H}\) is the parameters' function for the internal variable, \(\mathbf{x}\), and \(\mathbf{F}\) is the point safety factor. \(\mathbf{F}>1\) indicates that the zone is in the non-yielded state; \(\mathbf{F}=1\) indicates that the zone is in critical state; \(\mathbf{F}<1\) indicates that the zone is under the yielded state.  
Based on the Mohr-Coulomb failure criterion, the distance BC between the stress state and the strength envelope in Figure 2 can be considered as the strength margin. Accordingly, the point safety factor based on Mohr-Coulomb criterion can be defined as Equation (3):  
\[\mathbf{F}=\frac{|\mathbf{AC}|}{|\mathbf{AB}|}=\frac{|\mathbf{AD}|\cos}{|\mathbf{AB}|}=\frac{c\cos +\frac{\sigma_{1}+\sigma_{3}}{2}\sin}{\frac{\sigma_{1}-\sigma_{3}}{2}} \tag{3}\]
# Designing Procedure Execution Tools with Emerging Technologies for Future Astronauts
###### Abstract  
NASA's human spaceflight efforts are moving towards long-duration exploration missions requiring asynchronous communication between onboard crew and an increasingly remote ground support. In current missions aboard the International Space Station, there is a near real-time communication loop between Mission Control Center and astronauts. This communication is essential today to support operations, maintenance, and science requirements onboard, without which many tasks would no longer be feasible. As NASA takes the next leap into a new era of human space exploration, new methods and tools compensating for the lack of continuous, real-time communication must be explored. The Human-Computer Interaction Group at NASA Ames Research Center has been investigating emerging technologies and their applicability to increase crew autonomy in missions beyond low Earth orbit. Interactions using augmented reality and the Internet of Things have been researched as possibilities to facilitate usability within procedure execution operations. This paper outlines four research efforts that included technology demonstrations and usability studies with prototype procedure tools implementing emerging technologies. The studies address habitat feedback integration, analogous procedure testing, task completion management, and crew training. Through these technology demonstrations and usability studies, we find that low- to medium-fidelity prototypes, evaluated early in the design process, are both effective for garnering stakeholder buy-in and developing requirements for future systems. In this paper, we present the findings of the usability studies for each project and discuss ways in which these emerging technologies can be integrated into future human spaceflight operations.  
human-computer interaction; usability; augmented reality; Internet of Things; procedure execution
# Security Challenges for Light Emitting Systems
## 4 Results and Discussion
#### Choice of Reed-Solomon  
A comparison with other corrector codes was made [15]; the results of this comparison are presented in Table 3.  
The comparison is based on the speed of encoding/decoding of these codes; which is an important argument as these codes are often used to transmit information in real-time, on the efficiency to the noise resistance, and the ability to correct a larger number of errors.  
The security of encoding and decoding is also an essential consideration in the selection of Reed-Solomon codes. Other corrective codes, such as the Hamming code, are considerably more difficult to identify and fix transmission faults than Reed-Solomon codes, which detect problems the first time they occur.  
According to the table (Table 3), RS codes have a very high resistance to noise, which represents a major factor for our study; moreover, RS codes have a great capacity to correct the greatest number of transmission errors. These codes are also used in on-off keying modulation in the proposed architecture; hence, the choice of these codes for error correction. The variable p is an integer representing the probability of the existence of noise.
# Near-Real-Time Global Thermospheric Density Variations Unveiled by Starlink Ephemeris
## 1 Introduction
Numerous companies have proposed mega LEO communication constellations [31], with SpaceX's Starlink emerging as the largest LEO constellation. SpaceX began launching Starlink satellites in 2019, and as of October 2024, the constellation comprises over 6000 mass-produced small satellites in LEO. This Starlink constellation enables continuous global coverage, but the sheer size of the constellation poses significant collision risks [32; 33]. Several near-miss incidents between Starlink satellites and other spacecraft have been reported [34]. In May 2021, recognizing the need for international space safety and the collision risks associated with Starlink satellites, SpaceX began publishing predicted ephemeris data for Starlink satellites on space-track.org approximately every 8 h, facilitating collision avoidance efforts for space activities. In this study, we conduct a comprehensive assessment of the capability of Starlink predicted ephemeris data to capture thermospheric density variations. First, based on the unique characteristics of Starlink ephemeris data, we first apply a maneuver detection algorithm based on mean orbital elements, which allows us to investigate Starlink's routine orbital maneuvers and its responses to disturbed thermosphere during geomagnetic storms. Subsequently, based on energy conservation principles, we compute 1-min resolution mechanical energy loss for each Starlink satellite. By analyzing the relationship between mechanical energy loss derived from Starlink ephemeris data and thermospheric density changes from other satellites during both quiet and disturbed geomagnetic conditions, we evaluated the accuracy and reliability of this dataset.
# Thermally Induced Displacements and Rotations of Pillars for Precise Geodetic Measurements
## 3 Experimental Results  
We first present the measured displacements up to the top of the pillar in Figure 5. It is obvious that the temporal course of the displacements is closely linked to the temperature gradients shown in Figure 4. In both the heating and cooling phases, a total of 10 h of time history is shown with almost negligible displacements at the end of the observations.  
Figure 6 shows the rotations at two positions where the inclinometers were attached. The upper one was mounted on the 3 cm-thick insulating layer at the top of the pillar, while the lateral one was mounted on the cantilever near the top on the cooler (unheated) side of the pillar.  
Figure 5: Lateral displacements at the top.  
During the heating phase, the temperature of the inclinometer placed at the top of the pillar increased by 2.9 K, while that of the inclinometer attached on the angle bracket at the side rose by 2.0 K, as shown in Figure 7. It is evident that the top inclinometer experienced slightly greater heating than the side one, despite being placed on an insulation board made of extruded polystyrene. We need to stress also that the side inclinometer was firmly fixed while the top one was only put on the insolation layer above the top plate. These differences in attachment and positioning help to explain the temperature trends observed in Figure 7. While both inclinometer positions showed similar temperature changes with a maximum around 2.0 K, the top inclinometer showed a clear 50 min peak during the heating phase, during which the temperature rose to 2.9 K before dropping back to 2.0 K. This temperature fluctuation was observed during the heating phase and could be related to the positioning of the inclinometer at the top of the pillar, where ambient conditions or local radiant heat may have been more pronounced despite the insulating layer. In contrast, the side inclinometer, which was firmly fixed and partially shielded by the pillar itself, showed a more stable thermal response.  
Figure 8 shows the comparison between the measured displacements and those obtained from the measured rotations. The transformation coefficients and as explained in the previous section were used. The results show excellent agreement, which illustrates the suitability of measuring rotations instead of displacements, which is a much simpler and more convenient technique. We must emphasize that the results of the inclinometer  
Figure 6: Absolute values of relative rotations at two positions at and near the top.  
Figure 7: Temperature changes at inclinometers.  
attached to the side near the top obviously agree better. We attribute this to the better insulation and mounting of this inclinometer.