esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Discussion
### Periodic Frame Structure  
The periodically two-dimensional truss structure is shown in Figure 6. The whole calculation structure should be divided into 15 sub-structures, A1-A15, and the boundary is points P9 and P10 are clamped. The cross-section of each truss is a pipe, and the inner radius is 80 mm and 79 mm, respectively. A harmonic excitation is applied on point P1 in the z-direction. The Young's modulus, density, and Poisson's ratio are 210 GPa, 7800 kg/m\({}^{3}\) and 0.3, respectively. The mesh size of the finite element model is 0.01 m, there are 1864 beam elements in total, and the mass is 155.33 kg.  
The y- and z-direction displacements of point P2 in the 1\(\sim\)1000 Hz domain are compared with FEM results in Figure 7. The two results are in excellent agreement.  
In addition, the real and imaginary parts of force on point P7 in the z-direction are compared with FEM in Figure 8. It can be seen that both real and imaginary forces vary in the low-frequency range. This also means the vibration power flow is almost located in the low-frequency band.  
In addition, we also compared the natural frequencies of the truss structure with results from the literature [16]. The truss structure consists of twenty substructures as shown in Figure 2. The mesh size of each beam in the finite element model is 0.01 m.  
The frequency comparison results in Table 1 further verify the correctness of the calculation method in this paper.  
Figure 6: Scheme periodic frame structure.  
Figure 7: Dynamic responses of point P2 compared with FEM: (**A**) in z direction, (**B**) in y direction.
## 3 Results
### Performance of Alarm Algorithm  
To evaluate the performance of the alarm stage, the state of staying in bed is regarded as a positive condition while exiting the bed is negative. Therefore, the detection metrics are defined as follows:  
* TP: the bed-exiting scenarios are predicted with warning.
* TN: the staying-in-bed scenarios are identified as safe.
* FP: the staying-in-bed scenarios are predicted with warning.
* FN: the bed-exiting scenarios are identified as safe.  
The examples of the above four conditions are illustrated in Figure 11. The TP example was from scenario 17 when the patient exited from the bedside. The TN example was from scenario 1 when the medical personnel helped to put a safety vest on the patient. The FP example was from scenario 1 when the in-bed movement was identified as bed exiting.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **StrongSORT** & **DeepSORT** & **ByteTrack** \\ \hline Number of frames losing tracking & 21,482 & 8205 & 8205 \\ Lost-tracking rate & 23.4\% & 8.9\% & 8.9\% \\ Total count of ID changes & 1550 & 2109 & 1697 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of MOT methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **mAP50** \\ \hline All & 98.8\% \\ Medical Personnel & 98.6\% \\ Patient & 99.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Five-fold cross-validation results of customized YOLOv8.  
The FN example was from scenario 2 where the alarm was not triggered as tracking of the patient's head was lost when the patient slipped away from the side of the bed. The causes of lost tracking could be that the patient moved so fast that the frame rate of the depth camera could not capture it. Figure 12 displays the confusion matrices of the alarm algorithm combined with tracking results from DeepSORT and ByteTrack.  
Both results shown in Table 5 from the MOT methods DeepSORT and ByteTrack have excellent sensitivity with satisfactory specificity. The false-negative scenario where the alarm is not raised while the patient is at risk of a fall should be controlled at the lowest level in real hospital applications, which in our experiment happ  
Figure 11: Illustration of four conditions of confusion matrices, where the large red frame indicates the dynamic safe region, the pink frame indicates the head of user, and the small red frames indicate the head of medical personnel: (**a**) true-positive example; (**b**) true-negative example; (**c**) false-positive example; (**d**) false-negative example.  
Figure 12: Confusion matrices of alarm algorithm: (**a**) DeepSORT; (**b**) ByteTrack.
## 3 Results and Discussion  
Figure 4 shows the angular scan for the transmission gratings recorded in the 900 \(\upmu\)m photopolymer layers for which the composition is shown in Table 1.  
Figure 3: Shrinkage as a function of exposure time for photopolymer compositions A, B, C, D, and E in reflection experiments.  
Figure 2: Phase shift as a function of exposure time in transmission experiments for photopolymer compositions A, B, C, D and E.  
The standard photopolymer PA with AA and YE/TEA has a maximum diffraction efficiency (DE\({}_{\text{max}}\)) of 55%. We can obtain a less toxic photopolymer with YE substituting AA by NaAO and BMA by DHEBA. A good result with DE\({}_{\text{max}}\) = 72% is obtained for photopolymer PF. Substitution of AA and YE in the photopolymer PB yields a low DE\({}_{\text{max}}\) (41%). Adding the crosslinker DHEBA in photopolymer PC increase the DE\({}_{\text{max}}\) to 56%, which is similar to the Demax of the standard photopolymer PA. Optimization of the TEA concentration in biophotopol improves the performance with DE\({}_{\text{max}}\) = 74% for photopolymer PD. Additionally, using DHEBA in photopolymer PF makes it possible to obtain a higher diffraction efficiency DE\({}_{\text{max}}\) (77%).  
We fit the experimental data with the Kogelnik coupled wave theory and we obtained these values for the index modulation n\({}_{\text{1}}\) and the attenuation coefficient \(\alpha\) (\(\mu\)m\({}^{-1}\)). The detailed equations can be seen in [15,16]: PA (n\({}_{\text{1}}\) = 23.0 \(\times\) 10\({}^{-5}\), \(\alpha\) = 4.5 \(\times\) 10\({}^{-3}\)), PB (n\({}_{\text{1}}\) = 17.7 \(\times\) 10\({}^{-5}\), \(\alpha\) = 1.2 \(\times\) 10\({}^{-3}\)), PC (n\({}_{\text{1}}\) = 22.5 \(\times\) 10\({}^{-5}\), \(\alpha\) = 1.0 \(\times\) 10\({}^{-3}\)), PD (n\({}_{\text{1}}\) = 29 \(\times\) 10\({}^{-5}\), \(\alpha\) = 1.0 \(\times\) 10\({}^{-3}\)), PE (n\({}_{\text{1}}\) = 31 \(\times\) 10\({}^{-5}\), \(\alpha\) = 1.0 \(\times\) 10\({}^{-3}\)), PF (n\({}_{\text{1}}\) = 29.0 \(\times\) 10\({}^{-5}\), \(\alpha\) = 3 \(\times\) 10\({}^{-3}\)). The photopolymers PA and PF with the dye YE have an attenuation coefficient higher than photopolymers with PRF.
# Human Appropriation of Net Primary Production: From a Planet to a Pixel
## 4 Results  
### HANPP at the Planetary Scale  
The concept of \"human appropriation of the products of photosynthesis\" began in earnest with the 1986 paper by [PERSON] et al. (24) that estimated global terrestrial NPP at 60 PgCyr\({}^{-1}\), with low, intermediate, and high estimates of human appropriation of NPP of 2.36 PgCyr\({}^{-1}\) (3.9%), 18.5 PgCyr\({}^{-1}\) (30.7%), and 26.4 PgCyr\({}^{-1}\) (38.8%), using different definitions of \"appropriation\" of human managed ecosystems (Figure 1). [PERSON] et al. (16) estimated global terrestrial NPP at 56.8 PgCyr\({}^{-1}\) and a narrower range of human appropriation of NPP ranging from 8.0 to 14.8 PgCyr\({}^{-1}\) (14-26% of NPP) with great regional variation. [PERSON] et al. (2007) refined the definition of HANPP to include two components. HANPP (land use) is the difference between the potential NPP of terrestrial ecosystems in the absence of human land use and the actual NPP. This applies to a variety of forms of land degradation, such as deforestation, desertification, and soil erosion, as well as urbanization and infrastructure, but it can also be negative where intensive cultivation, usually involving irrigation, raises actual NPP beyond the natural photosynthetic potential of the land. Note, however, the methodological difficulties in measuring it identified above. HANPP (harvest) is the amount of biomass removed through crop and timber production and livestock grazing, with consideration of human-induced fires. They found global potential NPP of 65.5 PgCyr\({}^{-1}\) and HANPP of 15.6 PgCyr\({}^{-1}\), 23.8% of potential NPP, of which 40% is HANPP (land use), 53% is HANPP (harvest), and 7% is human-induced fires.  
Figure 1: Changing estimates and definitions of global HANPP.  
The work of [PERSON] et al. [10] illuminates these data from the global perspective of planetary limits and safe operating spaces within them. Alongside atmospheric carbon, reactive nitrogen and other planetary limits, HANPP emerges as a key planetary limit. [PERSON] (2012) argues persuasively that it is the most critical of these several limits; global NPP measures the photosynthetic capacity of Earth against which HANPP is increasingly pressing. [PERSON] et al. [25], for example, found that global HANPP doubled in the 20 th century from 13% to 25% of global potential NPP. Another doubling to half would entail enormous consequences for ecosystem services and biodiversity. Moreover, the agricultural and forestry activities that enable HANPP have reduced standing stocks of terrestrial carbon by half [25], while doubling its turnover time; this has displaced upwards of 450 PC from the terrestrial landscape to the atmosphere. These observations make HANPP an essential and underappreciated global scale sustainability indicator that improves upon \"land transformation,\" based on areal units, because it captures spatial variations in ecological productivity and the intensity with which humans appropriate it.
# Methodology for Verification of Geometrically Complex Components Through Reverse Engineering
## 4 Discussion  
When verifying the suitability of the welding fixture and its subsequent application in serial production, verification plays a key role. Traditional measurement methods, such as dimension measurement using micrometers or other measuring instruments, may be insufficient for accurately assessing all geometric deviations that can affect part positioning and welding accuracy. In this process, it is essential to consider not only basic dimensional tolerances but also more complex factors, such as proper component positioning, their mutual alignment, and potential deformations during welding. Reverse engineering combined with 3D scanning enabled a detailed analysis of the fixture and an evaluation of all these factors. The results obtained from PolyWorks Inspector confirmed that deviations from the CAD model were within acceptable tolerances, which would have been difficult to assess using conventional measuring tools. Figure 12: Illustration of a selected representative sample from the total surface area of the fixture. Table 3, referencing Figure 10, displays the results of flatness measurements of the fixture in individual planes. For each plane, the measured values, required tolerances, and actual deviations are provided. Based on these data, all measurements are within the specified tolerances (marked as \"Pass\"), confirming that the fixture meets the requirements for flatness and geometric accuracy. Table 4 presents the results of surface distance measurements during the verification of the welding fixture. Each surface was measured to assess flatness and deviation from the nominal value in comparison with the tolerance range of \(\pm\)1.000 mm. All measured surfaces, including Surf 1, Surf 5, Surf 9, and others, showed deviations within the permissible tolerances, indicated by the \"Pass\" status, confirming that the surface distances of the fixture met the requirements for implementation into production. Without these advanced methods, it would be extremely challenging to determine whether the fixture is truly suitable for serial production and whether it ensures the required quality of welded components. Given the accuracy and functionality demands of the manufacturing process, verification using reverse engineering is essential for ensuring the required quality and eliminating the risk of errors that could affect the entire production process. Based on the verification and testing of the welding fixture, although the results meet the required tolerances, there are several areas where the process could be further improved and optimized. The first step is to enhance the accuracy of component positioning. Automation of positioning systems, for example, through robotic arms or automatic clamping mechanisms, could improve the speed and precision of part placement, thereby reducing the risk of human errors. Another possible improvement is to enhance the mechanical and thermal properties of the fixture. The use of materials with better thermal properties or greater resistance to
## 2 Hydrogen Value Chain  
Some now argue that a true decarbonized economy cannot even exist without hydrogen. The European Commission is particularly determined to make Europe deliver on its ambitious climate promise to achieve carbon neutrality by 2050; in its main document related to the introduction of green hydrogen technology, it aims to give a boost to clean production, since this immensely versatile energy vector could find many possible applications in all major sectors of modern society--from industry to buildings to transportation [20].  
Figure 4 shows a generic scheme of hydrogen's value chain, hinting that to frame the implementation of this strategy, it is important to first define the configurations considered  
to be priorities in the hydrogen value chain--from production to final consumption. This comprises, in practice, three phases:  
* **Production.** The first stage of the hydrogen value chain consists in its production, with different pathways, processes, and associated technologies already identified. Depending on the required scale, large-scale (centralized) production is distinguished from small-scale (decentralized) production--ideally, close to the place of consumption;
* Road distribution in the form of liquefied/compressed gas, ending with a liquid-to-liquid refueling process, liquid-to-gaseous cryogenic storage systems, or gas-to-gas at various scales;
- Distribution by ships in the form of liquefied hydrogen, including delivery for end-use in oil pipelines and road transport;
- Distribution of gaseous hydrogen through pipeline systems;
- Blending of hydrogen with natural gas in the current natural gas infrastructure.
* **End-use.** In the third stage, the hydrogen value chain is addressed to the main end-use applications in the mobility/transport and industrial sectors. In residential and industrial stationary applications, mixtures of hydrogen and natural gas can be applied to generate heat and electricity.  
An utmost important matter on this last phase is with regards to the strategic configuration of the hydrogen value chain, i.e., how hydrogen going to be used as an energy vector.
## 3 Results and Discussions
### Release of Nutrients
It has been reported that enriching soil with a sustainable silicon source, such as rice straw biochar, containing approximately 10-20% silicon, can help to lower the aluminum concentration and enhance soil health and plant growth [53]. Therefore, it can be assumed that the abundant silicon within the rice straw biochar structure located in this composite would combine with the aluminum in LDHs, or even in substrate cultures, to form hydroxy aluminosilicate complexes and reduce the potential leachable aluminum [42]. The mechanisms of the profound magnesium release from the prepared composite are explained as follows: generally, the highly mobile nature of magnesium makes it susceptible to less strong binding to soil or growing media charges, which can be ascribed to its larger hydrated radius than that of other available cations, such as calcium, potassium, and sodium [54]. Furthermore, magnesium ions, which are present in the LDH structure, would be replaced by other exchanging positively charged ions in the substrate solution, particularly by calcium, which has the same valence as magnesium and is also abundant in alkaline substrates [55]. The substantial dependence on phosphorus as an indispensable ingredient of fertilizers, which has developed in agricultural production in response to increased food production, has led to an imminent depletion of phosphate rock reserves, which are the primary sources of phosphorus for agriculture. Furthermore, the current application of conventional phosphate fertilizers is repeatedly interrupted by runoff and leaching because of its high solubility. This means that the development of efficient phosphate fertilization practices has become increasingly important. Not only were P-BC-LDHs shown to have phosphate removal ability, but they also demonstrated positive effects on horticultural plant cultivation systems, in which both the agricultural product quality and availability of nutrients were improved. To confirm their practical application, more studies are necessary to further examine their longer release behaviors under natural field conditions.
# Autonomous Orbit Determination of LLO Satellite Using DRO-LLO Links and Lunar Laser Ranging
## 2 Measurement Model
### Lunar Laser Ranging Model  
Lunar laser ranging is a relay-based measurement, which includes the uplink signal from the satellite to the lunar corner reflector and the downlink signal from the lunar corner reflector to the satellite. If the two-way distance measurement time is marked at the satellite reception time \(t\), then the lunar corner reflector receives and forwards signals at the time \(t-\tau_{\text{d}}\) and the satellite transmission signal time is \(t-\tau_{\text{d}}-\tau_{\text{u}}\). \(\tau_{\text{d}}\) represents the downlink light and \(\tau_{\text{u}}\) represents the uplink light. Figure 4 shows a schematic diagram of the transponder ranging system.  
Figure 3: Schematic diagram of dual one-way ranging system.  
Ranging modeling requires the iterative solution of the downlink and uplink optical time equations. The downward light can be determined through fixed-point continuous iteration, with an initial value of \(\tau_{\rm d}^{(0)}=0\). The calculation formula is as follows [26]:  
\[\tau_{\rm d}^{(i+1)}=\frac{1}{c}\cdot\left|\mathbf{r}(t)-\mathbf{R}\big{(}t-\tau_{\rm d }^{(i)}\big{)}\right| \tag{4}\]  
where \(\mathbf{r}\) is the position vector of the satellite and \(\mathbf{R}\) is the position vector of the lunar corner reflector. Continuous iteration is conducted until the difference between two consecutive \(\tau_{\rm d}\) values is less than a specific threshold. In the simulation experiment, the specific threshold is taken as \(10^{-12}\). Similarly, the calculation formula for uplink light is as follows:  
\[\tau_{\rm u}^{(i+1)}=\frac{1}{c}\cdot\left|\mathbf{r}\big{(}t-\tau_{\rm d}-\tau_{ \rm u}^{(i)}\big{)}-\mathbf{R}(t-\tau_{\rm d})\right| \tag{5}\]  
The two-way distance measurement \(\rho\) is modeled as the sum of the up distance and down distance, that is:  
\[\rho=\rho_{\rm u}+\rho_{\rm d}=c\cdot(\tau_{\rm u}+\tau_{\rm d}) \tag{6}\]
# Time Evolution of CO2 Diffusivity of Carbonated Concrete
## 3 Results and Discussion  
### CO\({}_{2}\) Diffusivity of Concrete before Carbonation  
Figure 5 represents apparent and effective CO\({}_{2}\) diffusivity of noncarbonated concrete with time and the difference means the effect of moisture content on CO\({}_{2}\) diffusivity. Three points should be discussed. Firstly, for concrete with a high w/c ratio, the diffusivity was high, and the difference between apparent diffusivity and effective diffusivity was also large. This is coincided with experiment study of [PERSON] et al. [24]. This would be because high degree of cement hydration and high densification due to microstructure development with elapsed time for cement paste with a high w/c ratio. Secondly, all concrete showed that the CO\({}_{2}\) diffusivity decreased significantly until 28 days because the microstructural densification of the concrete is greatly developed until 28 days. The trend continued because of the ongoing reaction of cement hydration. The trend of the reduction lasted even at long term; however, the reduction rate has become modest. Thirdly, the effective CO\({}_{2}\) diffusivity was considerably smaller than the apparent diffusivity. It can be seen that pore closing due to moisture has great influence on the CO\({}_{2}\) diffusivity. The work has good agreement with [PERSON]'s work that carbonation rate of concrete exposed to the outdoor environment was greatly decreased due to raining [25].  
The CO\({}_{2}\) diffusivity is a material parameter that directly reflects the carbonation rate. If concrete is exposed to high CO\({}_{2}\) concentrations at early ages, carbonation can threaten concrete significantly. Therefore, extending the curing period could be a good solution in controlling the carbonation rate effectively.  
Figure 5: Apparent and effective CO\({}_{2}\) diffusivity of noncarbonated concrete. (**a**) w/c 0.45; (**b**) w/c 0.50; (**c**) w/c 0.55.  
Figure 6 illustrates the ratio of the effective diffusivity and the apparent diffusivity of concrete with w/c ratio. The apparent diffusivity decreases with time because of only development of microstructure of cement paste, while the effective diffusivity decreases with time because of remained amount of pore water after consumption due to cement hydration as well as the microstructural development. For this reason, the difference between two diffusivities was not significant at the beginning of age; however, the difference was obvious with elapsed time. The ratio had decreased significantly to the level of 0.1 to 0.2 after 28 days. At early ages, the difference between two diffusivities was not high because cement was not sufficiently unhydrated and pore was highly filled with water. However, the difference between two diffusivities was clearly noticed with elapsed time because water was continuously consumed by the hydration reaction of cement as it became hydrated. Therefore, it is very important to consider water content in estimating gaseous diffusivity of cementitious materials.
# Recycling Aged Asphalt Using Hard Asphalt Binder for Hot-Mixing Recycled Asphalt Mixture
## 3 Results and Discussion  
### Performance of Hard Asphalt Modified Aged Asphalt  
#### 3.1.1 Determining the Optimal Content of Lake Asphalt in Hard Asphalt  
The asphalt mixture with a higher modulus tends to have better rutting resistance to traffic. Generally, its modulus can be improved by using hard asphalt binder and additives. This study investigated the feasibility of using TLA as the modifier to increase the modulus of the recycled asphalt mixture. To this end, the dynamic modulus of the EME-20 mixture containing 50% RAP (EME-20@50%RAP) was determined. Its newly-added asphalt binder was hard asphalt made with 30% to 70% proportion of TLA, and the results are shown in Figure 5.  
According to Figure 5, the first finding is that the increasing TLA content in hard asphalt binder can significantly increase the modulus of the recycled mixture at every frequency. The optimal TLA content can be obtained when the modulus of RHMA reaches 14,000 MPa under 15 \({}^{\circ}\)C, 10 Hz. Therefore, the TLA content in hard asphalt binder was determined as 40%.