esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 How Bone Healing Occurs after Bone Injury
SO is characterized by pluristratified cords of \"stationary\" osteoblasts which differentiate by inductive stimuli [48; 49; 50; 51] at roughly constant distance from the capillaries (without moving during their transformation into osteocytes from the differentiation site); otherwise, DO is performed by the typical monostratified laminae of \"movable\" osteoblasts. The following events occur in sequence: firstly, variously polarized stationary osteoblasts (irregularly arranged inside cords) give rise, in the same place where they differentiate, to osteocytes (clustered within confluent lacunae), thus allowing the formation of preliminary thin trabeculae made up of woven bone that, due to their too-high cellularity, are not effectual from a mechanical viewpoint. Afterwards, along the surfaces of the SO-trabecular preliminary framework, dynamic osteogenesis occurs, which is mostly involved in filling primary haversian spaces, thus giving rise to primary osteons. DO-bone consists in lamellar bone which is mechanically more resistant compared to SO-trabecular bone, since it is less cellularized and arranged in a more orderly pattern; moreover, it occurs in relation to mechanical stimuli, instead of inductive vascular-derived factors (as occurs for SO). On the contrary, if a blood supply deficiency occurs, thus leading to low local oxygen rate, cartilage may form within the fibrous tissue; eventually, the cartilage, after hypertrophy and calcification, will be replaced by bone, as in endochondral ossification. In the case of bone repair by endochondral ossification, SO never seems to take place. In fact, the osteoblasts in contact with the remnants of the calcified cartilage are directly arranged in movable laminae [52] and all appear to be functionally polarized in the same direction, i.e., toward the calcified cartilage. Thus, in endochondral ossification, DO is not preceded by SO. At the end of the third stage, independently of the type of ossification (intramembranous or endochondral), the new bone that bridges the bone fragments is usually wider than the original bone profile. Once mechanical integrity has been re-established, the \"remodeling stage\" of the hard callus takes place. This represents the last stage of bone healing, which may lead to the recovery of the original anatomical shape. The balanced action of osteocl resorption and osteoblastic deposition is governed by Wolff's law and modulated by piezoelectricity, a phenomenon in which electrical polarity is created by pressure exerted in a crystalline environment [53].
## 4 Numerical Modelling and Validation
### Numerical Modelling
Table 4 exhibits the grid independence analysis, encompassing three grid types: coarse, medium, and fine. In the salvage of sunken ships, it is imperative to consider the tension and 6 DOFs of the largest lifting cables among the three vessels. The parameters examined in Table 4 include the MA of the roll response and the maximum tension of three representative lifting cables, namely, Nos. 1, 5, and 10. The table shows that the disparity between the calculation results of the coarse grid and fine grid exceeds that between the calculation results of the medium grid and fine grid. Generally, variation between the coarse and fine grids' calculation results ranges from 4% to 5%. In the numerical simulation of the salvage operation, the system composed of the three ships and the cables is relatively stable, so this difference is relatively large. Moreover, the salvage operation necessitates a high level of system security, as any errors could result in severe consequences. The tension difference in the No. 1 cable reached 5.2%, which is higher than those in the other cables. This discrepancy could be attributed to the positioning of the No. 1 lifting cable at the ship's bow, where it encounters waves first, leading to an intense response. If this difference interferes with the engineer's design plan, it could fail the lifting cables. In comparison, the difference between the calculation results of the medium grid and fine grid is smaller, typically around 2%, and the tension disparity of the lifting cables at the bow is considerable but falls within an acceptable range. Regarding the time aspect, the numerical simulation time for each stage (P1-P3) amounts to 25 s. These numerical simulations are executed on a supercomputer platform. Owing to the dynamic characteristics of parallel computing, difference in the number of computing nodes and cores dynamically allocated by the platform, specific algorithm used in STAR-CCM+ software, and other factors, the computing time is different under each working condition. By comprehensively considering the computation time for various working conditions in this paper, the computation time is reduced by about 43% for the coarse mesh and about 27% for the medium mesh compared to that of the fine mesh. The calculation time of the medium grid is reduced by around 27%. Fine grids impose higher demands on computing resources. Considering the calculation efficiency, the medium grid was chosen for the numerical simulation. Figure 3: The schematic diagram of the grid.
## 3 Results and Discussion
### Analysis of the Variation in Effective Properties According to the RVE Length
This trend is related to the fraction of the variation in CNTs greater than 2 \(\upmu\)m, as shown in Figure 14. As the length fraction of CNTs greater than 2 \(\upmu\)m increases, the effective properties increase and the variation range of the effective properties according to increases in the RVE length decreases. These findings demonstrate that the key factor affecting the effective properties of nanocomposites aligned in the \(x\)-direction is the length fraction of CNTs inside the RVE. Therefore, the selection of a suitable nanocomposite RVE length may only be achieved by ensuring that the RVE length is greater than the CNT length. In addition, because the effective properties converge to a constant value as the RVE length increases, the nanocomposite RVE must be selected considering the CNT length fraction according to the RVE length. Figure 14: Length fraction of CNTs greater than 2 \(\upmu\)m according to changes in the COV.
###### Abstract  
Kinematic orbit determination (KOD) of low earth orbit (LEO) satellites only using single-frequency global navigation satellite system (GNSS) data is a suitable solution for space applications demanding meter-level orbit precision. For some small LEO satellites with the sun-pointing attitude mode, the rotation of the GNSS antenna radiation pattern changes the observation noise characteristics. Since the rotation angle information of the antenna plane may not be available for most low-cost missions, the true elevation cannot be computed and a general elevation-dependent weighting model remains invalid for the onboard GNSS observations. Furthermore, the low-stability GNSS receiver clock oscillator of the LEO satellite at high speeds makes single-frequency cycle slip detection ineffective and difficult since the clock steering events occur frequently. In this study, we investigated the improved KOD strategy to improve the performance of orbit solution using single-frequency GPS and BeiDou navigation satellite system (BDS) observations collected from the Luojia-1A satellite. The weighting model based on exponential function and signal strength is proposed according to the analysis of satellite attitude impact, and a joint single-frequency detection algorithm of receiver clock jump and cycle slip is investigated as well. Based on the GPS/BDS-combined KOD results, it is demonstrated that the clock jump and cycle slip can be properly detected and observations can be effectively utilized with the proposed weighting model considering satellite attitude, which significantly improves the availability and accuracy of orbit solution. The number of available epochs is increased by 12.9% benefitting from this strategy. The orbital root mean square (RMS) precision improvements in the radial, along-track, and cross-track directions are 22.1%, 16.4%, and 6.5%, respectively. Combining BDS observations also contributes to orbit precision improvement, which reaches up to 28.8%.  
GPS; BDS; single frequency; low earth orbit; kinematic orbit determination; weighting model +
Footnote †: journal: Remote Sensing
# The Bearing Performance and Sectional Design Method of Yielding U-Shaped Steel Support
## 2 Analysis of Typical On-Site Failure Modes of Yielding U-Shaped Steel Support
### Typical Failure Analysis  
During the mining period of the 6315 working face, affected by the abutment pressure in advance and the influence of mining activities, the roadway within 50 m in front of the working face deformed seriously. The maximum convergence amount between the roof and the floor exceeded 450 mm. The U-shaped steel support did not show obvious yieldable deformation at the cable clamp nodes. Failure phenomena such as breakage of cable clamp bolts, fracture of the support at the nodes, and overall buckling deformation of the support occurred. The typical on-site failure situation is shown in Figure 3.  
Based on the analysis of the on-site failure characteristics, it can be known that:  
1. The bending moment at the nodes is large and is difficult to yield. The cable clamp nodes are designed near the bent parts of the support. At this position, the bending moment is large, and the cable clamp nodes are in an open state, making it difficult for the nodes to retract and yield.
2. Stress concentration occurs at the nodes, resulting in structural tearing. The failures at the cable clamp nodes are mostly manifested as the tearing of the support, the fracture of screw rods, the outward turning of cable clamps, and the breakage of clamping plates. The main reason is that the combined structure at the nodes is complex, stress concentration occurs easily, and structural failures are prone to happen at the nodes.
3. The support is subjected to asymmetric forces, resulting in buckling failures. Under the action of asymmetric loads, the support as a whole shows asymmetric failure characteristics. The deformation on the side of the small coal pillar is greater than that on the side of the solid rib. Straight-leg buckling and shoulder bending of the support occur, and the bearing capacity of the support is difficult to be fully exerted.  
Figure 3: Typical failure diagram of U-shaped steel in the field.  
Figure 2: U29 steel support design drawing (mm).  
In conclusion, in engineering projects under deep and complex conditions, yielding U-shaped steel supports are prone to stress concentration and large bending moments at the nodes, which leads to a difficulty for the nodes to retract and causes local structural failures. As a result, the overall bearing performance cannot be effectively exerted. To improve the yield effect at the nodes and enhance the bearing performance of the support, it is necessary to further clarify the internal force and deformation distribution states of yielding U-shaped steel support under complex stress environments.
## 5 Performance Evaluation  
### Simulation Setup  
#### 5.1.1 Satellite Constellation  
We chose the first phase of SpaceX's Starlink as the low earth orbit constellation as simulated in the system tool kit (STK), and the scenario of the simulation is shown in Figure 4. Starlink is a constellation of 72 orbits and 22 satellites in each orbit. The inclination of each orbit is \(53^{\circ}\), and the satellite's altitude from the ground is 550 km. As the satellite's altitude is only 550 km, we can consider that the earth is approximately flat on such a small scale. Using the trigonometric function, we can determine that the satellite can communicate with an area below that can be represented by a circle of radius of 573.5 km. Furthermore, we can find that the satellite can communicate with users whose straight-line distance is as far as \(\sqrt{573.5^{2}+550^{2}}\approx 794.6\) km.  
We defined the parameters of the satellite transmitter and receiver according to the technical documents submitted by Starlink to the federal communications commission (FCC) in 2017 [43]; its communication downlink frequency is 10.7-12.7 GHz, transmit ter equivalent isotropically radiated power (EIRP) is 10-12.88 dBW/MHz, and receiver power gain-to-noise-temperature (G/T) is 11.1-13.7 dB/K. In our scenario, we take the communication frequency as 12 GHz, EIRP and G/T take the maximum value, EIRP is 12.88 dBW/MHz, and G/T is 13.7 dB/K. After the constellation is fully deployed, the minimum elevation angle is 40 degrees, which means its communication range is determined accordingly. These parameters are also shown in Table 2.
## 3 Physiological Processes Mediating Cadmium Accumulation in Plants
### Cd Accumulation in Shoots and Grains  
In the final stage of Cd transportation, which consists of three processes, including xylem unloading, phloem translocation, and intervascular transfer, Cd accumulates in shoots and grains (the edible parts of cereals) [6]. Cd xylem unloading by effluxing Cd from vessels to parenchyma cells is the first step for the distribution of Cd in the shoot, as well as for the re-distribution of Cd via the phloem [85]. phloem translocation is the main pathway of Cd accumulation in plant shoots and grains. [PERSON] et al. [86] reported that the main complexes of Cd in phloem sap are with NA, GSH, and PCs. However, the manner in which these Cd chelates are loaded into the phloem is poorly understood, and no responsible transporter has been identified up to the present [87].  
Intervascular transfer in nodes is closely associated with the movement of Cd toward the grains [6]. In cereals, nodes are complex but well-organized vascular systems that consist of two major types of vascular bundles: enlarged vascular bundles (EVBs) and diffuse vascular bundles (DVBs) [6; 88]. EVBs stem from the lower nodes and are connected to leaves, whereas DVBs surrounding the EVBs start at the node and are connected to the upper nodes or panicles [6]. [PERSON] et al. [89] performed a noninvasive detection of Cd in rice plants and found higher Cd concentrations in nodes than in internodes. Increasing evidence suggests that phloem loading by intravascular transfer from EVBs to DVBs in node I is a major pathway for Cd movement toward grains in rice plants [88]. In rice, OsHMA2 and low-affinity cation transporter 1 (OsLCT1) are involved in intervascular transfer, with OsHMA2 loading Cd into the phloem of EVBs and DVBs and OsLCT1 exporting Cd from phloem parenchyma cells into the sieve tubes [90; 91]. However, the molecular mechanisms underlying Cd distribution in eudicot shoots are still unclear.
# SE-Lightweight YOLO: Higher Accuracy in YOLO Detection for Vehicle Inspection
## 5 Experiment
### Evaluation Method
#### 5.4.2 Evaluation Index  
Precision represents the proportion of correctly detected objections to the whole number of targets, which is denoted below in Equation (4):  
\[Precision=\frac{TP}{TP+FP} \tag{4}\]  
Recall is the proportion of correctly recognized targets to all the targets, calculated with Equation (5), as below:  
\[Recall=\frac{TP}{TP+FN} \tag{5}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration** & **Specific Version** \\ \hline CPU & Intel(R) Xeon(R) Silver 4208 CPU @ 2.10 GHz \\ Memory & 160 GB \\ GPU & NVIDIA Quadro RTX 5000 \\ Operation system & Windows 10 (64-bit) \\ Editing language & Python 3.8 \\ CUDA version & CUDA 11.7 \\ Deep learning framework & Pytorch 2.0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The training environment configuration for vehicle experiment.  
Figure 10: Diagram of the confusion matrix.  
The F1-score, also known as the Balanced Score, is defined as the reconciled average of precision and recall. It can be calculated using Equation (6):  
\[F_{1}=2\cdot\frac{Precision\cdot Recall}{Precision+Recall} \tag{6}\]  
The P-R curve is drawn with precision as the vertical axis and recall as the horizontal axis; the shaded area under the P-R curve is called the average precision value \(AP\), which is calculated as shown in Equation (7):  
\[AP=\int_{0}^{1}P(R)dR \tag{7}\]  
The \(AP\) values of each category are derived separately, and the average of all \(AP\) values can be averaged to obtain the average accuracy \(mAP\); the higher the \(mAP\) value, the better the generalization ability of the model, which is calculated as shown in Equation (8):  
\[mAP=\frac{1}{N}\sum_{i=1}^{N}AP_{i} \tag{8}\]  
\(N\) in the above equation is the total number of categories of the target; in this paper, \(N=6\) (i.e., six categories of person, car, bus, motorbike, bicycle, and truck) and \(AP_{i}\) is the average precision value of the \(i\)-th category.  
[EMAIL_ADDRESS] indicates the average accuracy value. The number 0.5 indicates an IoU (Intersection over Union) threshold of 0.5. In target detection, IoU is the degree of overlap between the detected target bounding box and the actual target bounding box. When the IoU threshold is 0.5, [EMAIL_ADDRESS] indicates the average accuracy under this threshold.  
[EMAIL_ADDRESS]/95 denotes the average precision at two different IoU thresholds. Specifically, \"[EMAIL_ADDRESS]\" denotes the average accuracy when the IoU threshold is 0.5, while \"[EMAIL_ADDRESS]\" denotes the average accuracy when the IoU threshold is 0.95. Typically, [EMAIL_ADDRESS]/95 is used to evaluate the performance of a target detection algorithm more comprehensively, as it takes into account the performance under different IoU thresholds. A higher value of [EMAIL_ADDRESS] indicates that the algorithm can accurately detect targets even under stringent IoU thresholds.
# Ontology Modeling Using Fractal and Fuzzy Concepts to Optimize Metadata Management
## 4 Case Study: Mold Assembly  
### FFOM Visualization and Analysis  
To demonstrate the practical application and structural advantages of FFOM, particularly its fractal characteristics, the MoldAssembly domain is visualized as a multi-part illustration in Figure 9. This approach explicitly showcases the principle of self-similarity inherent in FFOM's design. Figure 9a depicts the top-level MoldAssembly Fractal (FractalID: MA-1), along with its immediate subfractals, namely BaseAssembly (MA-1-1) and CoreAssembly (MA-1-2), representing the mold's primary structural components. These relationships are modeled using object properties, such as hasSubFractal, and are annotated with a TFN (e.g., (0.85, 0.9, 0.95)) and a timestamp (e.g., 2024-01-01T12:00:00Z) to capture the semantic strength and temporal context. Each fractal includes data properties, for instance, AssemblyID and TotalWeight for MoldAssembly, defining its core attributes.
## 5 Detection Methods: Modulation Spectroscopy for Wideband Absorption  
WMS has been demonstrated to have high sensitivity for sensing gas with an isolated spectral line. When the modulation index is small, WMS is approximately expressed as derivative spectroscopy. For the MIR spectral region, however, the fingerprint spectra are often broad, serried, crowded, and even overlap within the coverage of a tunable laser. To ensure the detection sensitivity and selectivity, the essential procedures include optimizing modulation index, varied modulation amplitude, removing fringes and noise interference, and multicomponent spectral fitting.
## 3 Materials and Methods
### Features Extraction and Selection
#### 3.3.1 Tweet to Sparse Feature Vector  
It retrieves sparse features such as word and character n-grams from tweets. There are options for excluding unusual features (n-grams appearing in fewer than m tweets for illustration) and adjusting the weighing mechanism (Boolean or frequency-based).  
* The word n-grams function extracts words from n = 1 to a specified highest value.
* Negations appended words that appear in negative scenarios and the prefixes do not affect the n-gram characteristics of words. The negative scope concludes with the following punctuated expression ([., ];[.];[.]+).
* Character n-grams return the number of characters.
* The part of speech (PoS) tags is processed using the Carnegie Mellon University (CMU) tweet Natural Language Processing (NLP) tool, which generates a vector space model based on the sequence of POS tags.
* Brown clusters convert the words in a tweet to Brown word clusters, resulting in a low-dimensional vector space model. It applies to n-grams of word clusters.
## 4 Conclusions  
This study demonstrated a TRP measurement procedure for spurious emissions and introduced three TRP measurement methods that are regulated by the FCC. Based on this, the SA-AS-TRP algorithm was proposed to analyze the effect of angular sampling on the TRP. The variables that affect the calculation of the TRP with these three methods, i.e., the HPBW of the spurious emissions and the sampling step, were determined through a simulation analysis. Therefore, it was assumed that the HPBWs of spurious emissions  
Figure 8: STDs of \(\Delta\)TRP as a function of the sampling step for the three methods. The HPBWs are in the range of \([2^{\circ},10^{\circ}]\).  
Figure 7: STDs of \(\Delta\)TRP as a function of the sampling step for the three methods. The HPBWs are in the range of \([10^{\circ},50^{\circ}]\).  
were in the ranges of \([10^{\circ},50^{\circ}]\) and \([2^{\circ},10^{\circ}]\). Numerical simulations demonstrated that the narrower the HPBWs of spurious emissions were, the smaller the measurement uncertainty caused by the sampling step would be. The steps needed to be less than \(6^{\circ}\) in order to ignore the measurement uncertainty for the spherical grid method. However, for the two-cut and three-cut methods, the measurement uncertainty of 2 dB and 1 dB should be considered in the two HPBW models. Moreover, the narrower the HPBWs are, the lower the measurement accuracy of the two-cut and three-cut methods will be.  
Qualitative analysis was applied to angular sampling for the three TRP measurement methods. In practical tests, the size of the frequency sweep range increases the time taken for the initial exploratory scan. In addition, it should be ensured that the beam peak is aligned with orthogonal cuts, which are beamformed according to how they are identified in exploratory scans for harmonics and spurious emission frequencies. In future work, an experimental verification should be conducted with a standard test site, test system, and terminal equipment.  
Methodology, G.G., N.M., R.W. and Y.X.; Software, G.G. and X.Y.; Validation, X.Y.; Resources, R.W. and Y.Z.; writing--original draft preparation, G.G. and X.Y.; writing--review and editing, N.M., R.W., Y.Z. and X.X.; Supervision, X.X. and P.Z. All authors have read and agreed to the published version of the manuscript.  
This research did not receive any specific funding.  
Not applicable.  
The authors declare no conflict of interest.