esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Open Challenges, Future Prospects and Research Directions
### Optimizing Technology Integration
Development of Compact and Cost-Effective Transceivers  
The development of compact, low-power, and cost-effective transceivers will be pivotal in making hybrid FSO systems more accessible and practical for widespread deployment. Also, addressing the cost barriers associated with deploying hybrid FSO systems will also be a significant area of focus. Miniaturization and Integration: Future research will focus on miniaturizing optical components and integrating them into single packages, reducing the overall footprint and power requirements. Advances in photonics and microfabrication technologies will facilitate the creation of lightweight and energy-efficient transceivers that can be easily installed in various environments, from urban infrastructures to remote rural areas. Cost Effectiveness for Broad Deployment: Research will aim to streamline manufacturing processes and explore innovative funding models to make these technologies affordable for various applications, including municipal networks, broadband access, and IoT infrastructures. The availability of cost-effective solutions will accelerate the adoption of hybrid FSO systems across different market segments. Supporting Emerging Applications  
With the advancement of hybrid FSO systems, they will possess significant potential to support emerging applications. 5G/6G Networks and IoT Integration: As hybrid FSO systems evolve, their potential to support emerging applications like 5G and 6G networks, as well as the IoT, will be significant. The integration of hybrid systems into these next-generation networks will enable uRLLC, and facilitate advanced applications such as autonomous vehicles, smart cities, and augmented reality. By providing high-capacity links that can dynamically adapt to varying conditions, hybrid FSO systems will be integral in meeting the demands of an increasingly connected world. Satellite Communications Synergy: Furthermore, hybrid FSO systems will play a crucial role in enhancing satellite communications by offering high-speed, low-latencylinks between satellites and ground stations. By combining FSO with traditional RF communication, satellite systems can achieve greater resilience and capacity, addressing the challenges of high-speed data transfer in diverse atmospheric conditions. Moreover, hybrid free-space optical communication systems hold significant promise for the future of Space-Air-Ground-Sea Integrated Networks (SAGSINs). These systems offer high bandwidth, low latency, and secure data transmission, making them ideal for enhancing connectivity in SAGSINs. Future advancements are expected to focus on improving link reliability, atmospheric compensation techniques, and seamless integration with existing RF systems to ensure robust, high-speed communication across diverse environments.
## 2 Measurement Principles and Device
### The Structure of a Cryogenic Vacuum Apparatus
#### 2.2.2 The Design of the Heater  
Since the temperature sensor of the sample heating stage is located within it, there is an inherent difference between the detected temperature and the actual temperature of the sample surface. Therefore, it is crucial to understand the temperature distribution within the sample heating stage. Utilizing COMSOL simulation, we modeled the variation of temperature at the center of the heating stage as a function of its thickness, while keeping the temperature of the heating film constant. The total thickness of the heating stage is 20 mm. The heating surface of the stage consists of a square heating film with a side length of 100 mm. On the opposite side of the heating stage is the sample placement surface, featuring a square area with a side length of 60 mm, centered at the same point as the heating stage's center, designed to hold the sample. The simulation mesh is shown in Figure 4.  
Figure 3: (**a**) Temperature drop curves of two blackbodies. (**b**) Design drawing of the blackbody.  
The initial temperature of the heating stage is 77 K. The heat source temperature can be freely set to \(-20\)\({}^{\circ}\)C, \(0\)\({}^{\circ}\)C, \(20\)\({}^{\circ}\)C, \(40\)\({}^{\circ}\)C, \(60\)\({}^{\circ}\)C, \(80\)\({}^{\circ}\)C, and \(100\)\({}^{\circ}\)C. Aluminum is selected as the material with an emissivity of 0.2 and a thermal conductivity of 238 W/(m\(\cdot\)K). The types of finite elements include triangular elements, tetrahedral elements, edge elements, and vertex elements. In each element, the node count for triangular and tetrahedral is just 0.5 and 0.2 by the default mode. The total number of elements is 255,811, and the total number of nodes is 60,275. The grid used in this paper is a free triangular grid, with grid sizes ranging from 0.03 mm to 3 mm. A steady-state solver is employed for the solution. Simulation results are shown in Figure 4b-d.  
As can be seen from the figure, the temperature difference between the sample surface and the heating surface is 0.01-0.02 \({}^{\circ}\)C.  
According to the simulation results, a hole was drilled in the flank side of the heating table with the depth of the radius of the circular heating table. A temperature sensor was installed in the hole near the sample surface to monitor the temperature of the sample mounting surface.
## 4 Results and Discussion
### VIRS DNB Observed DCC Reflected Lunar Radiance as a Function of Lunar Phase Angle
However, for the waning phase, the lunar zenith angle change progressively lagged behind as the lunar phase angle increases. This may have an impact on the lunar radiance comparisons with models, as discussed in Section 4.3. It is also noted that for NOAA-20, the relationship between lunar zenith angle and lunar phase angle are nearly identical to that of Suomi NPP as shown in Figure 5 (the data points overlapped), since these two satellites are in the sun-synchronous orbit with the same orbital plane, except with \(\sim\)50 minutes separation, and with the same local equator crossing time. As a result, near the equator, the lunar phase angle (as well as the lunar zenith angle) differences in the near-nadir-samples between Suomi NPP and NOAA-20 VIIRS DNB are within 0.43 degrees (given the lunar phase angle rate of 0.51 degree/h). This makes the direct comparison of the near-nadir-samples between Suomi NPP and NOAA-20 VIIRS DNB viable. It should be noted that due to earth rotation, the two satellites are not observing the same DCC cloud at nadir near the equator. Thus it is assumed that all DCCs have the same characteristics in reflectance, and any difference would contribute to the scatter in the plot. Figure 5: Comparison between lunar phase angle (LPA) and lunar zenith angle (LZA) for the November-December 2018 lunar cycle. Note that these angles from Suomi NPP and NOAA-20 have nearly identical patterns (data source: [[http://www.class.noaa.gov](http://www.class.noaa.gov)]([http://www.class.noaa.gov](http://www.class.noaa.gov))).
# Modulation Format Identification Based on Signal Constellation Diagrams and Support Vector Machine
## 2 Theory and Principle
### Extraction of Signal Constellation Diagram Features
\begin{cases}0,&s\leq a_{t}\\ \frac{s-a_{t}}{b_{t}-a_{t}},&a_{t}<s<b_{t}\\ 1,&b_{t}\leq s\leq c_{t}\\ \frac{d-s}{d_{t}-c_{t}},&c_{t}<s<d_{t}\\ 0,&s\geq d_{t}.\end{cases}  \tag{4}\]  
According to Equation (4), TMF divides all data points into five parts with intervals of \((-\infty,a_{t}]\), \((a_{t},b_{t})\), \([b_{t},c_{t}]\), \((c_{t},d_{t})\), and \([d_{t},+\infty)\), respectively. The points in the first and fifth parts have a weight of 0, while the points in the third part have a weight of 1. The points in the second and fourth parts have weights in the range \((0,1)\). In this paper, the values of \(a_{t}\), \(b_{t}\), \(c_{t}\), and \(d_{t}\) are estimated by Equation (5):  
\[ \begin{cases}a_{t}=s_{min}-1\\ b_{t}=\left\lvert\sqrt[3]{M}\right\rvert\\ c_{t}=max\bigg{\{}s\bigg{[}\bigg{(}\frac{M}{s}\bigg{)}+1\leq\bigg{(}\frac{M} {s-1}\bigg{)}\bigg{]}\bigg{\}}\\ d_{t}=s_{max}+1\end{cases}  \tag{5}\]  
Since the size of all the images we use is \(512\times 512\) pixels, we can obtain \(a_{t}=1\), \(b_{t}=8\), \(c_{t}=27\), and \(d_{t}=257\) by Equation (5). After the weight of the points \((x_{d},y_{d})\) are obtained. Fitting these points to the regression line \(y=ax+b\) via WLS. The FD of the image is calculated by Equation (6):  
\[D_{A}=\frac{(\sum_{r}w_{s}x_{d}y_{d})(\sum_{r}w_{s})-(\sum_{r}w_{s}y_{d})( \sum_{r}w_{s}x_{d})}{(\sum_{r}w_{s}x_{d})(\sum_{r}w_{s})-(\sum_{r}w_{s}x_{d}) ^{2}} \tag{6}\]  
The GLCM is an \(n*n\) square matrix, where \(n\) is the number of gray-level categories in the image. The \((i,j)\) elements in the matrix represent the number of pixel pairs. By calculating the GLCM in four directions \((0^{\circ},45^{\circ},90^{\circ}\)and \(135^{\circ})\), GLCM\([p(i,j)]\)is normalized by Equation (7):\[\frac{p(i,j)}{\epsilon}\Rightarrow\hat{p}(i,j) \tag{7}\]  
where \(\epsilon\) is the constant of normalization. Angular second moment \(m_{1}\), entropy \(m_{2}\), contrast \(m_{3}\), and correlation \(m_{4}\) are used to perform image block retrieval, which can be calculated by [24] Equation (8):  
\[ \begin{cases}m_{1}=\sum_{i}\sum_{j}P(i,j)^{2}\\ m_{2}=-\sum_{i}\sum_{j}P(i,j)\log P(i,j)\\ m_{3}=\sum_{i}\sum_{j}(i-j)^{2}P(i,j)\\ m_{4}=\frac{\left[\sum_{i}\sum_{j}((i*j)*P(i,j))-\mu_{x}\mu_{y}\right]}{\sigma _{x}\sigma_{y}}\end{cases}  \tag{8}\]  
where: \(\mu_{x}=\sum_{i}\sum_{j}i*P(i,j)\), \(\mu_{y}=\sum_{i}\sum_{j}j*P(i,j)\), \(\sigma_{x}=\sum_{i}\sum_{j}(i-\mu_{x})^{2}*P(i,j)\), \(\sigma_{y}=\sum_{i}\sum_{j}(j-\mu_{y})^{2}*P(i,j)\). The final texture features in this paper are calculated using the mean and variance of \(m_{1}\), \(m_{2}\), \(m_{3}\), and \(m_{4}\).
## 6 Q5: Future Direction
### Build Underwater Automatic Battery Redarging Module for Future UIoT  
Based on the research study in Section 3, the devices or nodes in the UIoT environment have limited resources. Additionally, it is difficult to recharge in a constrained underwater environment. In effect, it reduces battery life and network lifetime if any one of the nodes is dead. In [267], [PERSON] et al. introduced a metal-free sodium-seawater battery (Na-SWB). In [268], [PERSON] et al. proposed a battery degradation prediction and power optimization mechanism for surface buoys based on sea batteries. In [269], [PERSON] et al. proposed a rechargeable seawater battery (SWB) mechanism that produces energy from seawater. Finally, in [338], the Miresearch group developed battery-free sensor nodes for underwater exploration. Therefore, to solve the battery issues in UIoT, it is necessary to build an undersea battery or an automatic recharging mechanism or deploy battery-free nodes.
# A Novel SINS/SRS/CNS Multi-Information Fusion Global Autonomous Navigation Method
## 3 The Multi-Information Fusion Autonomous Navigation System Based on SINS/SRS/CNS  
First, the frames used in this section are defined:  
\(i\) frame--inertial coordinate frame. It is fixed with the inertial space and can be used as a reference to describe the Earth and carrier angular motion.  
\(g\) frame--east-north-up geographic coordinate frame. It is the navigation coordinate frame in the nonpolar regions.  
\(e\) frame--Earth coordinate frame. It is fixed with the Earth and can be used as a reference to describe the carrier position.  
\(b\) frame--body coordinate frame. The direction relationship between the \(b\) frame and the navigation system is the carrier attitude.  
\(G\) frame--grid coordinate frame. It is the navigation coordinate frame in the polar regions. The angle between the grid north and the geographic north is the grid angle, \(\sigma\).  
\(c\) frame--calculation coordinate frame. It is the navigation coordinate frame described by SINS, which contains the platform error angle.  
The relation diagram for the above frames is shown in Figure 2.  
Figure 1: The multi-information fusion global autonomous navigation scheme diagram for UAVs.
# Study of Effective Length Factor of Frame-Core Wall Structure with Cross-Layer Columns
## 5 Stability Analysis of Frame-Core Wall Structure with Cross-Layer Columns
### Buckling Load and Coefficient  
The buckling bearing capacity and buckling coefficient of the frame-core wall with cross-layer columns in the first-order buckling mode when the overall instability occurs is shown in Table 8. The overall buckling capacity of the structure decreases with the increase in the number of cross-layer columns and the number of stories. The buckling coefficient increases with the increase in the number of cross-layer columns and the decrease in the number of stories.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Role** & **Direction** & **Model 1** & **Model 2** & **Model 3** & **Model 4** \\ \hline \multirow{2}{*}{Earthquake} & X-direction & 9.0390 & 9.0240 & 9.0630 & 9.0590 \\  & Y-direction & 2.2520 & 2.2320 & 2.2580 & 2.2410 \\ \multirow{2}{*}{Wind load} & X-direction & 7.6630 & 7.6740 & 7.6910 & 7.7270 \\  & Y-direction & 2.0120 & 2.0090 & 2.0200 & 2.0250 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Stiffness-to-weight ratio of frame–core wall structures.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \(\mathbf{R_{FW}/N}\) & \(\mathbf{R_{F}/N}\) & \(\mathbf{R_{F}/RF}\) & \(\mathbf{R_{W}/N}\) & \(\mathbf{R_{W}/RF}\) \\ \hline
1 & 6.1728 \(\times\) 10\({}^{8}\) & 2.7850 \(\times\) 10\({}^{8}\) & 0.4512 & 3.3878 \(\times\) 10\({}^{8}\) & 0.5488 \\
2 & 6.1514 \(\times\) 10\({}^{8}\) & 2.7734 \(\times\) 10\({}^{8}\) & 0.4508 & 3.3781 \(\times\) 10\({}^{8}\) & 0.5492 \\
3 & 6.1516 \(\times\) 10\({}^{8}\) & 2.7695 \(\times\) 10\({}^{8}\) & 0.4502 & 3.3821 \(\times\) 10\({}^{8}\) & 0.5498 \\
4 & 6.1123 \(\times\) 10\({}^{8}\) & 2.7457 \(\times\) 10\({}^{8}\) & 0.4492 & 3.3666 \(\times\) 10\({}^{8}\) & 0.5508 \\ \hline \hline \end{tabular} Note: \(R_{FW}\), \(R_{F}\), and \(R_{W}\) are representative values of gravity loads borne at the bottom of the frame–core wall structure, frame columns, and core shear wall, respectively.  
\end{table}
Table 7: Reaction forces at the bottom of frame and core shear wall.
## 3 Results and Discussion  
### Homology Modeling  
The LRP1 receptor has been shown to bind to a large array of biologically significant ligands [83], making investigation of binding interactions with LRP1 critical [84]. Because the crystal structure of LRP1 has yet to be elucidated, we performed homology modeling to obtain a model of LRP1 using Phyre2. This program was able to generate a model structure based on the same region of the low-density lipoprotein receptor (LDL) ectodomain (PDB ID: 1N7D) [85] having a sequence identity of 36% and a confidence level of 100%. In previous work, Phyre2 also was used to predict the structure of wild-type carbonic anhydrase II with a sequence identity of 36% [86]. The sequence template homology of LRP1 found ranges from amino acid residues 933 to 1581 which contain the cysteine-rich complement-type repeat 3-10 (CR 3-10) of Cluster 2 (CCR2) of LRP1. The homology model agrees with previous studies that have identified LDL receptor to be structurally similar to LRPs [87]. The results are shown in Figure 1. As seen in Figure 1a, the original structure generated from Phyre2 is folded back. This is because this structure was modeled from LDL at endosomal low pH levels where the receptor adopts a closed conformation. This causes the CRs to associate with the \(\beta\)-propeller region to release its bound LDL and prevent apolipoprotein E (ApoE) and other ligands from binding [88] Since the structure of the LRP1 receptor has not yet been fully elucidated, it is unknown whether it also adopts a closed conformation similar to the LDL; however, studies have shown that the CRs are the most active regions for ligand binding, while the \(\beta\)-propeller region is mainly only essential in dissociation of ligands in endosomes. Since the aim of this work was to examine the interaction of tau protein with the LRP1 receptor, the \(\beta\)-propeller region that was arching over the CRs was truncated in order to allow binding of tau. The final structure, containing the CR 3-10 of CCR2 (Figure 1b), was then used to determine itsaffinity to the tau protein, since previous studies have shown that the LRP1 CCR2 cluster is involved in ligand binding.
# The Fate of _Yersinia pseudotuberculosis_ in Raw Fermented Meat Products
## 3 Results  
Two different types of raw fermented meat products were manufactured and analyzed to assess the survival of _Y. pseudotuberculosisis_. Not surprisingly, both the initial concentration and the length of storage (including their interaction) were highly statistically significant factors (\(p<0.001\)). In Tewurst, _Y. pseudotuberculosisis_ survived throughout the storage period (for 37 d) when the initial concentration was around 8 log CFU/g (Table 2). The numbers gradually decreased throughout the storage by 4.7 log CFU/g. During the fermentation period, the counts dropped by 1.3 log CFU/g. Another highly significant decrease was noted between Day 30 and Day 37, suggesting that the numbers would drop below the LOD not long after. However, in sausages with lower initial concentrations, the numbers of _Y. pseudotuberculosisis_ declined much more quickly, falling below the LOD on Day 10 for most of the batches. The counts in the final product were approx. 1.4 and 2.4 log CFU/g for initial concentrations of 3 log and 6 log, respectively. Even when the counts dropped below 2 log CFU/g, _Y. pseudotuberculosisis_ was still viable in at least one out of six samples and could be recovered by plating after pre-enrichment. In dry fermented sausage, the survival of _Y. pseudotuberculosis_ was clearly more hampered by the proliferation of the starter culture. In batches with initial concentration of 3 and 6 log CFU/g, no sample was positive on Day 2 of manufacture (Table 3). In batches starting with 8 log CFU/g, complete inactivation was delayed by only 2 d. The counts on Day 2 differed significantly between Teewurst and dry fermented sausage in batches starting with 8 and 6 log CFU/g (\(p<0.001\)). The differences between the two types of fermented products could be attributed to variations in pH, and water activity (a\({}_{\mathrm{w}}\)), and fat content. The first two d of fermentation resulted in the most pronounced decrease in pH for both products (\(p<0.001\)). The pH values dropped lower in dry fermented sausage than in Teewurst (Figure 1), which can be explained by differences in temperature and RH during manufacture, chemical composition, and diameter of the products. The pH values in dry fermented sausage gradually decreased to around 4.5, which is the minimum value enabling growth under otherwise optimal growth conditions [12]. The decrease in water activity due to drying was also much more pronounced in dry-fermented sausage compared to Teewurst (Figure 2), which would also act as a barrier for _Yersinia_ proliferation. The highest decrease was observed between Day 4 and Day 7 (\(p<0.001\)), corresponding with the onset of more intensive drying. The value 0.93, stipulated by Czech legislation as the maximum for dry fermented sausages [11], was achieved on Day 7. However, since _Yersiniae_ were already completely absent on Day 4 with a\({}_{\mathrm{w}}\) being 0.95-0.96, it is clear that water activity was not the key parameter in the elimination of _Y. pseudotuberculosis_ in our experiment.
## 3 Computation and Analysis
### Correlation Analysis Between GPS/Galileo Frequencies
It is clear from the figure that correlations exist between multipath errors for all frequency pairs, though the strength of this correlation decreases as the frequency differences increase. Additionally, the relationship between frequency difference and the absolute value of the correlation coefficient is illustrated in Figure 9, offering a more intuitive visualization of how frequency differences affect correlation strength. Due to the lack of low-elevation observations at the Galileo E5a frequency from the KERG station, the low correlations between E5a and the other frequencies in the KERG_KRGG baseline may not accurately reflect the true correlations. Apart from this, the residuals from frequency pairs, such as overlapping ones like L1/E1 and L5/E5a between GPS and Galileo, as well as non-overlapping frequencies like L2 and E5b, and the Galileo frequencies E5a, E5b, and E5 ab, exhibit relatively large correlation coefficients, indicating strong correlations. Other frequencies also demonstrate strong to moderate correlations between narrow-spaced frequencies. These strong correlations suggest that their residuals contain similar multipath errors, supporting the potential for combining frequencies to enhance multipath error correction. Figure 8: Absolute values of correlation coefficients between all frequencies of the baselines MATE_MAT1 (**a**) and KERG_KRGG (**b**). Utilizing a color gradient ranging from dark to light to represent the hierarchical levels of correlation strength, spanning from very strong to very weak or no correlation. Figure 9: Variation of all correlation coefficients with frequency differences for the baselines MATE_MAT1 (**a**) and KERG_KRGG (**b**).
# Role of Cyclin-Dependent Kinase 1 in Translational Regulation in the M-Phase
## 4 CDK1 Substitutes for mTOR Control of Cap-Dependent Translation
In HeLa cells the inhibition of CDK1 resulted in reduced phosphorylation of the ribosomal protein S6 (RPS6), a p70S6K substrate, indicating that CDK1 supports the initiation of translation through the p70S6K signaling pathway. However, CDK1 likely does not act via mTOR, as CDK1 inhibition did not alter the integrity of the cap binding complex [13]. Figure 4: Schematic representation of direct extramitotic effect of cyclin dependent kinase 1 (CDK1) on molecular axis influencing mRNA translation during M-phase. CDK1 activates the mammalian target of rapamycin(mTOR) and subsequently mTOR phosphorylates and activates its substrates ribosomal protein S6 kinase B1 (p70S6K) and LA-related protein 1 (LARP1). Phosphorylation of eukaryotic translation initiation factor 4E (eIF4E)-binding protein 1 (4E-BP1) by mTOR leads to the dissociation of 4E-BP1 from eIF4E and to the activation of the eIF4E complex. CDK1 directly phosphorylates the mTOR substrates 4E-BP1, p70S6K and LARP1. CDK1 exerts inhibitory phosphorylation of the eukaryotic elongation factor 2 kinase (eEF2K) and, as a consequence, the activity of elongation factor 2 (eEF2) is stimulated. The activity of ribosomal protein L12 (RPL12) is also CDK1-dependent.