esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection and Attribution of Greening and Land Degradation of Dryland Areas in China and America
## 3 Results
### Dryland Areas in China
#### 3.1.4 Drivers for Green and Desertification  
We observed widespread green in Chinese drylands (Figure 8a). We find that CC is the dominant factor accounting for 44.5%. CO\({}_{2}\) and LU are a little smaller (35.3% and 26.1%). For NC, CO\({}_{2}\) plays the most important role here (35.1%). LU and CC are almost the same (28.7% and 27.7%). For SW and NW, the ecological vegetation is fairly sensitive to climate. Climatic warming and humidification here will obviously affect the vegetation. So, we conclude that CC is the dominant factor in the vegetation growth of SW and NW (61.3% and 46.8%). In the dryland that experienced desertification in China (Figure 8b), a negative component of LU is the dominant factor. Even though the average values of CC and CV are smaller than LU, climate plays an important role in desertification. For most areas, CC plays positive effects on vegetation growth.
# Long-Period Grating with Asymmetrical Modulation for Curvature Sensing
## 3 Experiments and Results
By differentiating the phase matching condition with respect to temperature, the temperature sensitivity can be given as  
\[\frac{d\lambda}{dT}=\frac{d(n_{co}^{eff}-n_{cl}^{eff})}{dT}\Lambda+(n_{co}^{ eff}-n_{cl}^{eff})\frac{d\Lambda}{dT} \tag{5}\]  
The first component of Equation (5) is the thermo-optic effect of temperature sensing, in which the effective index of the core mode and cladding mode are both affected by temperature changing. Such thermal-optic effect can be shown as,  
\[ \begin{array}{l}\frac{dn_{cl}^{eff}}{dT}=\alpha_{co}n_{co}^{eff}\\ \frac{dn_{cl}^{eff}}{dT}=\alpha_{cl}n_{cl}^{eff}\end{array}  \tag{6}\]  
where \(\alpha_{co}\) and \(\alpha_{cl}\) are the thermo-optic coefficient of the core and cladding mode, respectively. The second component of Equation (5) is the thermal-expansion effect in the \begin{table}
\begin{tabular}{c c c c} \hline
**Sensor Structure** & **Curvature Range (m\({}^{-1}\))** & **Bending Sensitivity (nm/m\({}^{-1}\))** & **Reference** \\ \hline LPFG in three-core fiber & 0 to 0.588 & Maximum 3.234 & [16] \\ Spatial cascaded orthogonal LPFG & 0 to 2.0 & Maximum 11.568 & [17] \\ V-shaped LPFG & 0.5 to 2.2 & 16.315 and 23.085 & [19] \\ Interfaced tilted LPFG & 0.997 to 2.997 & \(-\)5.14 and 9.16 & [8] \\ Side-grooved LPFG & 0 to 2.52 & Maximum 27.76 & [24] \\ Asymmetric LPG & 0 to 4.59 & 29.4 & This work \\ \hline \end{tabular}
\end{table} 
Table 1: Comparisons between the LPG sensors for vector bending measurement. Figure 9: (a) Curvature response of the sample with offset of 6 \(\upmu\)m in four bending directions. (b) Wavelength response of the temperature measurement. temperature sensing, which causes the period of the grating to change. However, the thermal-expansion coefficient of the fiber is much lower compared to the thermo-optic coefficient; thus, this effect is negligible. The temperature sensitivity is mainly dependent on the difference between \(\alpha_{co}\) and \(\alpha_{cl}\). If such a difference is large, the temperature sensitivity would be very high; otherwise, it will be quite low. Figure 9b shows the wavelength response against temperature, from which we can see that the resonant wavelength exhibits a linear response to rising temperature. The temperature sensitivity is estimated to be 0.087 nm/\({}^{\circ}\)C.  
Since both curvature and temperature cause the resonant wavelength to shift, the influence of temperature response to the curvature measurement should be estimated. The temperature cross-sensitivity can be calculated by \(\mathrm{S_{temp}}/\mathrm{S_{curv}}\), where \(\mathrm{S_{temp}}\) and \(\mathrm{S_{curv}}\) are the temperature and curvature sensitivities, respectively. The cross-sensitivity is calculated to be 0.003 m\({}^{-1}\)/\({}^{\circ}\)C, which is quite low. That indicates that the influence of temperature during the curvature measurement can be ignored.
# Effect of Structural Materials on Monopropellant Thruster Propulsion Performance in Micro Scale
## 1 Introduction
As chemical propulsion involves energy conversion from chemical energy to thermal energy and then to kinetic energy for thruster generation, it is naturally affected by heat transfer characteristics, particularly in the micro scale, where there is excessive heat loss due to the large surface-to-volume ratio, which has been one of the main causes of performance degradation in propulsion systems in such scale. Furthermore, monopropellant thrusters are even more likely to compromise propulsion performance in the micro scale due to them having a catalyst in a catalyst bed, the reactivity of which is also affected by surrounding temperatures. From this point of view, both the compatibility of the materials with different micro-manufacturing processes and the thermal energy conservation and heat transfer characteristics of the materials are important factors in the development of downsized propulsion systems. However, despite numerous studies having attempted various fabrication processes and materials for microthrusters, little research has been conducted investigating how fabrication materials affect the propulsion performance of thrusters downsized to the micro scale. In this work, heat transfer and the corresponding propulsion performance characteristics of thrusters in the micro scale have been investigated. MEMS fabrication processes and materials that had been reported as compatible with a microthruster fabrication process were considered as structural materials for a numerical comparative propulsion performance analysis. In order to detail the thermal flow characteristics of propellant through a microchamber and supersonic micronozelle, as well as the heat transfer across microthruster structures, which affected propulsion performance depending on the properties of the materials used, a numerical heat transfer and microfluidic flow estimation was conducted. Propellants with different weight percentages (and enthalpies) were also considered to determine the effect of the amount of chemical energy stored in a propellant on propulsion performance while experiencing heat energy loss. In order to provide a useful reference for downsized space propulsion system development in regard to excessive heat energy loss in such a scale, the results of the simulation were analyzed, which included an examination of the thermal diffusivity of each material and the heat loss and inner nozzle flows with their different sensitivities to thermal diffusivity.
## 4 Analysis and Results
### Power Law Fit Using Curve_Fit  
Figure 3 shows the power law fits we obtain using _curve_fit_. We see how the fit for the IoTeX VG network provides a value of \(\alpha=2.61\) in accordance with Equation (1), hinting at the existence of a scale-free network. The IOTA VG, with an \(\alpha=1.93\) does not reach a value of two. When we focus on the HVG, we see an \(\alpha=2.03\) and \(2.01\) for IOTA and IoTeX networks, respectively. We can even talk of incipient scale-free networks in both HVG cases. We highlight as well how in the four subplots the power law fit has a maximum degree from which there is no power law fit.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Network** & **Nodes** & **Edges** & **Isolates** & **Self Loops** & **Density** & **Transitivity** \\ \hline IOTA VG & 1493 & 5715 & 0 & 0 & 0.005 & 0.300 \\ IoTeX VG & 1122 & 4472 & 0 & 0 & 0.007 & 0.312 \\ IOTA HVG & 1493 & 2969 & 0 & 0 & 0.003 & 0.344 \\ IoTeX HVG & 1122 & 2225 & 0 & 0 & 0.004 & 0.354 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Initial description of the visibility graph-based networks.
# Impacts of Micro-Deviations of Aperture on the Characteristics of Collision Atomization Field
## 5 Results and Discussion  
### Cavitation Experiment of Internal Flow Field  
Previous investigations on the influence of nozzle diameter are usually carried out at the same time as the research on the influence of the nozzle aspect ratio, and it is concluded that it is more difficult for the initiation and development of cavitation at a higher aspect ratio. To the best of our knowledge, a separate study on the influence of diameter and its sensitivity level has not been reported, especially for the colliding orifices. In this paper, eleven pairs of nozzles with design diameters varying in the range of 0.95 mm\(\sim\)1.00 mm\(\sim\)1.05 mm are used to study the influence of small diameter differences, and the measurement data are detailed in Table 1.  
Firstly, the cavitation phenomenon of the internal flow field is simulated. According to the simulation results, the range of injection pressure in the experiment is set as 0.2 MPa \(\sim\)0.8 MPa. In this range, the internal flow field will appear in four states: initial cavitation, cavitation development, supercavitation and jet. Figure 9 shows the cavitation state of the flow field in the 1 mm orifice when the aspect ratio is 4 in the experiment. The experimental phenomena of the flow field in other apertures can be seen from Ref. [35]. It can be seen from Figure 9a that when the jet pressure is 0.2 MPa, there is no cavitation phenomenon in the orifice, the two jets are very soft and the collision is not intense. When the pressure reaches 0.3 MPa [see Figure 9b], cavitation begins to appear in the orifice and the collision between the two jets becomes intense. With the increase in pressure [see Figure 9c], the cavitation area in the orifice becomes larger and the jet collision becomes more intense. Until the pressure is 0.4 MPa [see Figure 9d], the cavitation area in the orifice almost reaches the maximum, which is very close to the supercavitation state and almost reaches the most intense collision state. When the pressure increases to 0.8 MPa [see Figure 9e], the internal flow field has reached the state of jet flip, and the two jets return to a relatively stable state again. The experimental results are consistent with the simulation results detailed in Ref. [36].  
Figure 8: Structure of the single injector.  
According to the cavitation experimental results, the increase in the nozzle diameter leads to the gradual increase in the critical pressure at the beginning of cavitation, and the pressure required for super-cavitation and hydraulic turnover will be higher. On the contrary, the nozzle with smaller aperture has more chance to reduce cavitation, which is due to the changes in pressure and velocity. When other factors remain unchanged, the smaller the diameter, the higher the fluid velocity and the lower the local pressure at the orifice inlet; therefore, the easier it is to reduce to the saturated vapor pressure, thereby promoting cavitation.
## 5 Evaluation of Major Design Codes Based on Results of FE Analysis
### Influence of Arch Rib Spacing  
The effects of arch rib spacing on the normalized critical axial forces of the three types of bridges are shown in Figure 16. The figure demonstrates that, according to the Chinese code, the normalized critical axial force reaches relatively small peaks when the stiffness ratio is at a certain arch rib spacing, while it decreases with increasing arch rib spacing when the stiffness ratio is relatively large. The arch rib spacing has a slight influence on normalized critical axial force according to the Japanese code. The normalized critical axial force specified by the Eurocode increases with decreasing arch rib spacing. Thus, the Chinese code overestimates the positive effect of arch rib spacing at smaller spacings and underestimates it at larger spacings when the stiffness ratio is small, while otherwise, the same as the Eurocode, underestimates the positive influences of arch rib spacing. The influence of arch rib spacing is estimated properly in the Japanese code, especially if the stiffness ratio is relatively small.  
Figure 15: Accuracy Evaluation of Multinational Codes on Influence of \(\gamma\)-value.  
Two aspects of these results can be considered of crucial importance. On the one hand, arch rib spacing has a slight effect on the critical axial force, according to FE analysis. Therefore, the sensitivity of normalized critical axial stress to the variation of arch rib spacing is basically similar to the codes. On the other hand, the Chinese code takes arch rib spacing into account by adopting the geometrical moment of inertia of the two arch ribs \(I\) and their spacing \(b\) as parameters, while the influence of spacing \(b\) is factored by \(1/l_{\text{b}}\) which relates to the stiffness of the lateral bracing. Therefore, when the stiffness ratio is relatively small, the dual effect of \(I\) and \(b\) results in a peak in critical axial force at certain spacing. Otherwise, spacing \(b\) is the main effect, which results in the critical axial force decreasing with increasing arch rib spacing. In the Eurocode, an increase in arch rib spacing causes the increase in \(\eta\)-value shown in Figure 5, which leads to a higher \(\beta\)-value and hence a reduced critical axial force of the arch rib. On the contrary, in the Japanese code, the integral lateral stiffness is taken into account while calculating the transverse radius of gyration, \(r\), which means greater arch rib spacing results in greater integral lateral rigidity of the arch rib. Therefore, the critical flexure load according to the Japanese code increases with increasing arch rib spacing. However, the change is small.
# A Software Defined Radio Evaluation Platform for WBAN Systems
## 4 Implementation and Demonstration
### Discussion  
For NB communications, the proposed evaluation platform supports seven RF bands from 400 MHz-2.4 GHz. However, for the UWB communications, 11 RF bands from 3494.4 MHz-9984.0 MHz with a 499.2 MHz bandwidth, as shown in Table 7, can not be covered by the FMC111 RF module. Meanwhile, both NB and UWB utilize BCH as the coding method for the communications. The proposed platform supports all the configurations for BCH encoding and decoding required by the standard. Moreover, there are eight methods of modulation that can be configured in the platform: \(\frac{\pi}{2}\)-DBPSK, \(\frac{\pi}{2}\)-DBPSK, \(\frac{\pi}{4}\)-DQPSK, and GMSK for NB communications and on-off signaling, Continuous Phase Fre-quency Shift Keying (CP-BPSK), wideband FM, and DPSK for UWB communications. The proposed evaluation platform shares the blocks for the spreading factor, bit interleaver, scrambler seed, SRRC filter, low-pass filter, and DDC for both NB and UWB channels, since they have identical configurations.
# Quantum Circuit Implementation of Multi-Dimensional Non-Linear Lattice Models
## 6 Extension to Two- and Three-Dimensional Lattice Models
### Modified Full Adders for Combined \(u^{2}+v^{2}\) Evaluation  
For the combined evaluation of (mantissa of) \(u^{2}+v^{2}\), the shift-and-add-based approach is used in a modified form as compared with the evaluation of \(u^{2}\) for one-dimensional lattice models. As before, quantum full adders, as introduced by [PERSON] (here, controlled by ancilla qubit \(|anc\rangle\)) form the building block. The clear, modular structure was the motivation to use this type of full adder.  
For the combined evaluation of \(u^{2}+v^{2}\), a number of modifications to the full adder are introduced that can be summarized as follows:  
* For \(N_{M}\) mantissa qubits for both \(u\)- and \(v\)-velocity, a \((2N_{M}+1)-\)qubit output register is needed to remove the potential of overflow;
* In the shift-and-add approach, \(N_{M}-1\) shifts will be used as in the one-dimensional \(u^{2}\) evaluation;
* In each of the \(N_{M}\)-controlled addition operations for \(u\) and \(N_{M}\)-controlled additions steps for \(v\), the controlled additions add \(N_{M}\) mantissa qubits into an \((N_{M}+1)\)-qubit register;
* For \(N_{M}=4\), the modified full adders are defined starting from a 5-qubit full adder. The modification is based on a specialization of the \((N_{M}+1)\)-qubit input register based on the fact that the most significant qubit will be \(|0\rangle\);
* A modular structure is maintained by a pair-wise arrangement of the qubits defining \(u\)- and \(v\)-velocity mantissa, as demonstrated later. This means that now \(MAJ\) and \(UMA\) blocks appear defined to act covering four qubits (however, one qubit will never be acted on).  
Figure 10 shows the quantum-circuit implementation of the modified adders used to add 4-qubit \(u\)-input (top of figure) as well as 4-qubit \(v\)-input into the 6-qubit register \(|r05|\ldots|r00\rangle\). Modified sub-circuits \(MAJ_{u}\) and \(MAJ_{v}\) are derived from \(MAJ\) by accounting for the 1-qubit additional spacing introduced by the second set of input qubits interleaved with the first set of input qubits. Similarly, sub-circuits \(UMA_{u}\) and \(UMA_{v}\) can be derived directly from \(UMA\). As can be seen in the circuit for the addition of \(v\)-input at the bottom of Figure 10, for the least-significant inputs \(|v0\rangle\), the original \(MAJ\) and \(UMA\) sub-circuits need to be used since only three qubits are involved in these steps. The modified full adders shown here form the basis for the mantissa squaring used in the quantum-circuit design based on quantum floating-point arithmetic described in the next sections.
## 4 Recent Advances and Future Directions
### Privacy-Preserving Machine Learning  
With the growing concern over data privacy, especially in light of stringent regulations like the GDPR and CCPA, privacy-preserving ML has become a focal point of research and development. Techniques such as differential privacy, federated learning, and homomorphic encryption have been developed to protect individual privacy while still enabling the use of ML on sensitive data [320; 321; 322].  
Federated learning, in particular, has gained significant traction, as it allows models to be trained on decentralized data sources such as smartphones or IoT devices, without the need to transfer raw data to a central server [324]. This approach not only enhances privacy but also reduces the risk of data breaches. For instance, companies like Googlehave employed federated learning in their mobile devices to improve predictive text algorithms without compromising user privacy. Homomorphic encryption, which allows computations to be performed on encrypted data, is another technique that is gaining attention. Although computationally intensive, advancements in this area are making it more feasible for practical applications [325; 326; 327; 328].  
Looking ahead, the future of privacy-preserving ML will likely focus on making these techniques more efficient and scalable. There is also growing interest in developing new cryptographic protocols that combine the strengths of different privacy-preserving techniques to provide robust and flexible solutions. Additionally, as more data move to the edge (e.g., in IoT and mobile devices), there will be a greater emphasis on ensuring that ML models can operate securely and privately in decentralized environments [329; 330; 331; 332].
## 5 Discussion and Conclusions  
In this paper, a continuous-time explosion fragments motion estimation method is proposed. We balanced the FOV and the frame-rate of the HFR camera, designed a high-speed vision system, and captured the moment of the explosion and the motion of the high-speed fragments. Basing on our frame-rate strategy, an adjusted bundle adjustment, i.e., SBA and BMA are proposed for trajectory estimation of high speed. With the optimized trajectory, a cubic B-spline representation of the continuous-time position, velocity and acceleration is proposed for this fast motion. Furthermore, the momentum and force can also be estimated at any given time. With these quantization parameters, the damage assessment of fragments can be evaluated.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \(3\sigma\) **of PW\({}_{\mathbf{x}}\)** & \(3\sigma\) of PW\({}_{\mathbf{y}}\) & \(3\sigma\) of PW\({}_{\mathbf{y}}\) & **Momentum** & **Force (N)** \\  & **(mm)** & **(mm)** & **(mm)** & **(kg m/s) \(\times\) 10\({}^{-4}\)** & **Force (N)** \\ \hline
1 & 63.2 & 22.5 & 29.1 & 8.04 & 0.0019 \\
2 & 31.4 & 31.5 & 43.3 & 6.19 & 0.0015 \\
3 & 59.5 & 43.2 & 24.3 & 10.2 & 0.0024 \\
4 & 32.1 & 27.0 & 32.8 & 6.62 & 0.0016 \\
5 & 48.0 & 19.2 & 51.2 & 7.39 & 0.0018 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The uncertainty data of five fragments using synchronization frame-rate bundle adjustment (SBA).  
Figure 8: Estimated position (in blue) with \(3\sigma\) bounds (red and orange) in three axes; (**a**) is estimated by SBA + B-spline, (**b**) is estimated by BAM + B-spline.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \(3\sigma\) **of PW\({}_{\mathbf{x}}\)** & \(3\sigma\) **of PW\({}_{\mathbf{y}}\)** & \(3\sigma\) **of PW\({}_{\mathbf{y}}\)** & **Momentum** & **Force (N)** \\  & **(mm)** & **(mm)** & **(mm)** & **(kg m/s) \(\times\) 10\({}^{-4}\)** & **Force (N)** \\ \hline
1 & 79.8 & 33.6 & 42.8 & 8.04 & 0.0019 \\
2 & 22.8 & 29.2 & 30.6 & 6.20 & 0.0015 \\
3 & 69.8 & 43.2 & 18.0 & 10.1 & 0.0024 \\
4 & 61.  
The experiments show that our adjusted bundle adjustment cooperated with B-spline representation, high accuracy and robust position, that the velocity and acceleration can be estimated at any given time, and that the related error of the estimated motion can be controlled under 1% by using high frame-rate cameras.  
Conceptualization, Y.N. and F.L.; methodology, Y.N.; validation, Y.W., supervision, X.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Not statement.  
The authors declare no conflict of interest.