esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Longitudinal Dynamics Vehicle Model  
The vehicle model must consider the important effect related to powertrain control while at the same time being as simple as possible for real-time use. In particular, the model must reproduce the energy consumption of the vehicle, which is mainly controlled by the aerodynamics, rolling resistance, track grade, and forward acceleration. Furthermore, the model must reproduce the wheel slip condition since the VCU has to handle it. To reproduce such effects, a model for contact forces is necessary, for which a correct evaluation of normal forces is required. As such, the model must reproduce the load transfer when accelerating or braking or when climbing on a graded track. The adopted model is depicted in Figure 6. It is a planar model that reproduces the longitudinal dynamics of the vehicle. It has a total of 10 degrees of freedom (DOF) with 2 constrained degrees of freedom (CDOF). The system consists of four rigid bodies:  
* 1 bogie with 3 DOF
* 1 carbody unit with 1 DOF
* 2 wheelsets with 3 DOF each  
Figure 6: Scheme of the vehicle model, highlighting connections between bodies and the adopted degrees of freedom.  
The carbody unit is constrained by a pin to rotate with respect to the bogie. The pitch motion of the carbody unit is balanced by secondary suspension springs and dampers. The bogie motion consists of longitudinal motion \(x\), heave motion \(z\), and pitch rotation \(\theta\). The bogie is connected to wheelsets by means of longitudinal and vertical springs and dampers, representing the bushing and the primary suspension elastic behavior, respectively. The wheelset can spin about its axis and contact forces are applied to reproduce the wheel-rail contact mechanism as better detailed in the following. Both the rolling resistance and aerodynamic forces are considered. Rail contact is modeled considering the contact point vertical displacement (considered as a CDOF) and rotation due to rail grade. Table 1 presents the adopted DOF of the system and the nomenclature. The equation of motion for the system is written by adopting the minimal set approach, where the equations of motion are in the form of the following:  
\[[M]\ddot{q}+[C]\dot{q}+[K]q=Q \tag{4}\]  
where \(q\) is the vector containing the system DOF (see Table 1), \([M]\) is the mass matrix, \([C]\) is the damping matrix, \([K]\) is the stiffness matrix, and \(Q\) is the vector of all the external forces applied to the system.
# Encapsulated Probiotics: Potential Techniques and Coating Materials for Non-Dairy Food Applications
## 4 Biomaterials Utilized for Probiotics Encapsulation
### Carbohydrate Polymers
#### 4.1.4 Starch  
Starches have received great attention in the probiotic encapsulation process because they are generally recognized as safe, abundant, inexpensive, non-allergenic, able to produce a gel with a strong and flexible structure, transparent, colorless, flavorless, and odorless gel that is semi-permeable to water, carbon dioxide, and oxygen [76]. Probiotics can survive in gastrointestinal and colon environments when embodied in the starch granules [40]. Moreover, the utilization of starch with combinations of biomaterials, such as alginate and chitosan, was reported to protect the probiotics [41]. Chemically modified starches (e.g., succinated, cross-linked, substituted, oxidized, and acid-treated) possess higher solubility and better mechanical properties and have also been used in probiotic encapsulation [41; 76]. Starch, i.e., resistant starch, can also serve as a potential prebiotic since this type of starch cannot be digested in our small intestines. The prebiotic effects of resistant starch allow a higher release of the probiotics in the large intestine. The adherence of the probiotics is also higher with resistant starch due to its robustness and resilience to environmental stresses [1]. Starch adhesion increases the initial cell load of probiotics and improves the targeted delivery of probiotics. However, starch often exhibits high viscosity in solution. Thus, it negatively affects the efficiency of encapsulation [67]. The starch viscosity can be adjusted through starch modifications.
## 4 Experimental Setup and Results
The performance of the proposed scheme, subject to nonlinear impairments of long-haul transmission links, was experimentally explored, and the results are presented in Figure 17. As shown, the launch powers of the PDM-QPSK signals over a 2000 km link, the PDM-8 QAM signals over a 2000 km link, the PDM-16 QAM signals over a 1040 km link and the PDM-32 QAM signals over a 400 km link were varied in the ranges of \(-4\)-\(6\) dBm, \(-3\)-\(6\) dBm, \(-3\)-\(8\) dBm, and \(-2\) -\(7\) dBm, respectively. In Figure 17, EVM, as a function of launch power, was also plotted to evaluate the system's overall performances with respect to different modulation formats. Note that, in comparison with other modulation formats, the EVM variation for 32 QAM over the whole optical launch powers range was reduced because of the relatively short link length. As seen in the figure, when the launch power was higher than 2 dBm, all the transmission systems operated at the nonlinear region, where the system performance was dominated by fiber nonlinearity. Over such a region, the correct identification rates of 100% were still achievable for the four modulation formats. On the other hand, when launch powers were decreased to \(-4\) dBm and \(-3\) dBm for the PDM-QPSK and PDM-16 QAM signals, respectively, as a direct result of the resulting reductions in OSNR, the correct identification rates for the PDM-QPSK and PDM-16 QAM signals were reduced with decreasing launch power. The experiment results indicated that the proposed MFI scheme was robust against fiber nonlinearities and suitable for long-haul transmission links. Figure 16: Correct identification rates under different OSNR values in back-to-back experiments. The vertical dash lines indicate the OSNR thresholds corresponding to 7% FEC correcting BER of \(3.8\times 10^{-3}\).
## 3 Precision Sowing Conditions of consistent Sowing-Depth Regulation  
Precision seeding technology has been developed in developed countries for decades, from the initial precise seed discharge and smooth seed-guiding technology of the seed rower to the research on the seed falling into the seed bed with precision technology, gradually developing to consistent sowing-depth regulation, and now to the variable sowing, according to the soil environment. At present, China's precision sowing technology research needs to catch up. At this stage, China's precision seeding technology includes specific research into the precise seed discharge, smooth seed-dropping, and the seed falling into the seed bed with precision. More and more attention is paid to the consistency of sowing depth. The formation of the sowing depth results from the joint action of the furrow opener and the soil covering and compacting, so the soil covering and compacting, which consistently have an important influence on the sowing depth, will also become the development direction of our precision seeding technology. The furrow-opener depth control method is mainly realized by the imitation mechanism driving the sowing monomer to undulate with the terrain; at present, the main ways of the sowing monomer regulating the furrow-opener depth are as follows (Table 2):  
As an essential part of guaranteeing consistent sowing depth, stable soil covering and proper soil compaction will become vital elements of precision-seeding technology research.
## 2 Materials and Methods
### Analytical Methods
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Soil** & **Distance from Firing Line (m)** & **Depth (cm)** & **pH** & **Cu** & **Ni** & **As** & **Cd** & **Pb** & **Zn** \\ \hline \multirow{6}{*}{6 th shooting range} & \multirow{3}{*}{250} & 0 & 8.78 & 1,073 & 27 & 0 & 27 & 28,040 & 267 \\  & & 5–15 & 8.80 & 927 & 10 & 0 & 10 & 24,043 & 170 \\  & & 15–30 & 8.77 & 463 & 10 & 0 & 7 & 19,763 & 63 \\ \cline{2-10}  & & 0 & 8.03 & 200 & 10 & 0 & 3 & 5127 & 0 \\  & & 5–15 & 8.30 & 183 & 13 & 0 & 3 & 4147 & 0 \\  & & 15–30 & 8.48 & 9 & 2 & 5 & 0 & 185 & 0 \\ \cline{2-10}  & & 0 & 8.02 & 400 & 7 & 13 & 7 & 11,137 & 0 \\  & & 5–15 & 8.52 & 193 & 13 & 7 & 7 & 5297 & 0 \\  & & 15–30 & 8.64 & 217 & 13 & 3 & 3 & 4293 & 0 \\ \hline \multirow{6}{*}{3 rd shooting range} & \multirow{3}{*}{250} & 0 & 8.94 & 643 & 10 & 0 & 3 & 20,030 & 90 \\  & & 5–15 & 8.80 & 580 & 10 & 0 & 3 & 13,987 & 137 \\  & & 15–30 & 8.76 & 623 & 10 & 0 & 3 & 17,303 & 60 \\ \cline{2-10}  & & 0 & 7.50 & 273 & 13 & 13 & 13 & 6203 & 0 \\  & & 5–15 & 8.62 & 273 & 13 & 0 & 3 & 5313 & 0 \\  & & 15–30 & 8.67 & 210 & 13 & 0 & 3 & 7557 & 0 \\ \cline{2-10}  & & 0 & 8.30 & 650 & 7 & 3 & 3 & 13,043 & 0 \\  & & 5–15 & 8.52 & 533 & 7 & 0 & 3 & 11,093 & 10 \\  & & 15–30 & 8.48 & 413 & 10 & 0 & 3 & 8140 & 3 \\ \hline \multirow{6}{*}{Drain ditch} & \multirow{3}{*}{100} & 0 & 7.95 & 377 & 10 & 0 & 3 & 5417 & 3 \\  & & 5–15 & 8.28 & 493 & 10 & 0 & 3 & 6977 & 23 \\  & & 15–30 & 8.62 & 573 & 7 & 0 & 3 & 8740 & 63 \\ \hline \multirow{6}{*}{Background} & \multirow{3}{*}{0} & 0 & 6.09 & 3 & 6 & 0 & 2 & 14 & 46 \\  & & 5–15 & 6.04 & 3 & 5 & 0 & 2 & 21 & 45 \\ \cline{1-1}  & & 15–30 & 6.03 & 5 & 8 & 1 & 2 & 33 & 44 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The total Cu, Ni, As, Cd, Pb, and Zn concentration (ppm) and pH in soils of shooting ranges. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Soil** & **Distance from Firing Line (m)** & **Depth (cm)** & **208 Pb/204 Pb** & **207 Pb/204 Pb** & **206 Pb/204 Pb** & **206 Pb/207 Pb** & **208 Pb/207 Pb** \\ \hline \multirow{6}{*}{6 th shooting range} & \multirow{3}{*}{200} & 0 & 38.77 & 15.69 & 18.53 & 1.18 & 2.47 \\  & & 5–15 & 38.21 & 15.45 & 18.26 & 1.18 & 2.47 \\  & & 15–30 & 38.96 & 15.65 & 18.50 & 1.18 & 2.49 \\ \cline{2-10}  & & 0 & 38.09 & 15.53 & 18.46 & 1.19 & 2.45 \\  & & 5–15 & 38.22 & 15.53 & 18.63 & 1.20 & 2.46 \\  & & 15–30 & 39.08 & 15.77 & 18.62 & 1.18 & 2.48 \\ \cline{2-10}  & & 0 & 38.90 & 15.88 & 18.60 & 1.17 & 2.45 \\  & & 5–to which distilled water was added, left for 1 h while stirring occasionally with a glass rod, and read within 60 s using a calibrated pH meter (XL-20, Thermo Fisher Scientific, Fair Lawn, NJ, USA). The soil pH was measured using a suspension of 25 g of soil in 25 mL of water, that is, a soil/water ratio of 1:1. The elements Cu, Ni, As, Cd, Pb, and Zn were mapped using an electron probe micro-analyzer (EPMA, EPMA-1600, Shimadzu, Tokyo, Japan) at the Korea Basic Science Institute (KBSI), Jeonju Center, South Korea, and a wavelength-dispersive X-ray (WDS) system.
# A Hybrid User Mobility Prediction Approach for Handover Management in Mobile Networks
## 5 Experimental Results
### Mobility Data Description  
There are several human mobility data types including mobility data generated in cellular networks, WiFi networks, social networks, and global positioning system (GPS). We deployed GPS data since it has the highest localization accuracy. To conduct our experiments and to analyze the impact of the proposed method on different users, we chose mobility data from open street map (OSM). We deployed GPS traces that were uploaded by different users and are publicly available ( [[https://www.openstreetmap.org/traces](https://www.openstreetmap.org/traces)]([https://www.openstreetmap.org/traces](https://www.openstreetmap.org/traces)), accessed on 12 October 2020). For each user \(i\), a sequence of past GPS trajectories \(L_{useri}=(l_{t-ht},...,l_{t-2},\)\(l_{t-1})\) was provided as the input of the mobility model. A mobility predictor uses this information as prior knowledge of user mobility behaviour and tries to learn the repetitive patterns within. These sequential data are time-stamped data that contain information regarding time, longitude, latitude, and elevation of each location point in the user trajectory. Hence, \(l_{t}\) in a user input sequence that contains information on longitude (\(x_{t}\)), latitude (\(y_{t}\)), and elevation (\(z_{t}\)) at time step \(t\) (i.e., \(L_{useri}=((x_{t_{1}},y_{t_{1}},z_{t_{1}}),(x_{t_{2}},y_{t_{2}},z_{t_{2}}),...)\)).
## 2 Error Sources Analysis of Antenna Holographic Measurement
### Feed Offset Error  
In Equation (1), the feed offset error affects \(\Delta\varphi\), so we need to construct the optical path difference model \(\Delta\) caused by the feed offset through geometric optics and obtain \(\Delta\varphi\) through the equation \(\Delta\varphi=k\Delta\).  
The optical path difference caused by the feed axial offset \(\delta_{a}\) is schematically shown in Figure 4a, and the relation can be expressed as [24]:  
\[\Delta_{a}=\delta_{a}(1-\cos\theta)=\delta_{a}\left(1-\frac{1-\tan^{2}\frac{ \theta}{2}}{1+\tan^{2}\frac{\theta}{2}}\right)=2\delta_{a}\frac{r^{2}}{4f^{2}+ r^{2}} \tag{4}\]  
where \(\delta_{a}\) is the axial focal offset of the feed.  
As shown in Figure 4b, the optical path difference caused by the lateral offset of the feed is [25]:  
\[\Delta_{l}=\rho-\rho^{\prime}\approx\delta_{l}\sin\theta\cos\phi^{\prime}= \delta_{l}\frac{4 fr}{4f^{2}+r^{2}}\cos\phi^{\prime} \tag{5}\]  
where \(\delta_{l}\) is the lateral focal offset of the feed.  
Figure 3: Microwave holographic measurement error sources.  
Figure 2: The 26 m radio telescope.
# Multi-Sensor Precipitation Estimation from Space: Data Sources, Methods and Validation
## 5 Satellite Precipitation Products
### Precipitation Products with High Spatiotemporal Resolution
PMW data are acquired from various Microwave Imager sensors including GMI, SSM/I, AMSR-E, AMSR2, and MADRAS sensors; IR data are provided by NCEP/CPC. Currently, the Precipitation Measuring Mission ([[https://sharaku.eorc.jaxa.jp/GSMaP_NOW/index.htm](https://sharaku.eorc.jaxa.jp/GSMaP_NOW/index.htm)]([https://sharaku.eorc.jaxa.jp/GSMaP_NOW/index.htm](https://sharaku.eorc.jaxa.jp/GSMaP_NOW/index.htm)), accessed on 1 December 2024) offers access to the GSMAP products through its Science Team at JAXA. The PERSIANN currently comprises the following products: PERSIANN (0.25\({}^{\circ}\)/1 h), PER-SIANN-CCS (0.04\({}^{\circ}\)/1 h), PERSIANN-CDR (0.25\({}^{\circ}\)/daily), and PERSIANN-CCS-CDR (0.4\({}^{\circ}\)/3 h). The precipitation estimation algorithm employed by PERSIANN utilizes an artificial neural network-based approach that incorporates infrared-imagery-derived cloud information, adjusted using passive microwave data [28; 123]. The PERSIANN-CDR product is an infrared-based climate monitoring product trained using radar data and normalized to GPCP monthly totals [143]. The estimation of precipitation in the PERSIANN CCS product involves establishing distinct relationships between infrared brightness temperature and precipitation through the use of PMM; these estimates are further calibrated using co-located cloud images and gauge-corrected radar rainfall data corresponding to each classified cloud group [29]. Finally, the generation of the PERSIANN-CCS-CDR product begins with the rain rate outputs from the PERSIANN-CCS model, which are subsequently adjusted using GPCP V2.3 [144]. The production of the comprehensive PERSIAN dataset is carried out by the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine ([[http://chrsdata.eng.uci.edu/](http://chrsdata.eng.uci.edu/)]([http://chrsdata.eng.uci.edu/](http://chrsdata.eng.uci.edu/)) accessed on 1 December 2024).
# Vegetation Dynamics and Recovery Potential in Arid and Semi-Arid Northwest China
## 3 Discussion  
### Characteristics of Vegetation Greening in Northwest China  
This study found that the NPP and NDVI of vegetation in northwest China generally showed an increasing trend during 1982-2019, consistent with the findings of [PERSON] et al. [11]. A significant shift in the NDVI was observed in 1999, with post-1999 increases being more significant than those before 1999. Vegetation changes in northwest China exhibited clear spatial heterogeneity. Both the NDVI and LPJ-simulated NDVI indicated that the areas showing increases were primarily located in ecologically sensitive regions, including eastern and southern Inner Mongolia, Ningxia, southern Gansu, southeastern Qinghai, and northwestern Xinjiang. In contrast, a decreasing trend was observed in southern Xinjiang, northern Qinghai and Gansu, and western Inner Mongolia. The overall trend in vegetation cover in northwest China showed positive growth, with significant increases mainly concentrated in northeastern Inner Mongolia, northwestern Xinjiang, eastern and southern Qinghai, and southern Gansu. However, it is worth noting that the central desert regions of northwest China still had very low vegetation coverage.  
In addition to human activities, changes in vegetation cover in northwest China are mainly influenced by topography and the regional climate [36; 37]. In areas close to mountains, the high base level of precipitation and the warming and humidifying climate favor vegetation improvement [38]. In desert regions, however, temperature increases outweigh precipitation increases [39]. The rise in temperature leads to higher evaporation rates, which offset the benefits of increased precipitation, resulting in insignificant vegetation growth or even degradation [40]. Furthermore, in the context of global warming, melting glaciers are affecting vegetation in areas through which meltwater flows [41]. Increased precipitation or meltwater can enhance grass growth, making it taller and more lush [42], a phenomenon particularly evident in the Iii region of Xinjiang. In the Iii region, western Northern Xinjiang, and eastern Gansu, agricultural areas have the potential to expand, and water resource issues have been alleviated. However, these regions' capacity to handle increased precipitation is limited. Excessive rainfall can have adverse effects, significantly impacting agricultural production and ecosystem growth. This is especially true for certain crops and livestock farming, which may be notably affected by changes in precipitation patterns [43].
## 3 Results and Discussion
### Mechanical Properties  
#### 3.5.1 Hardness  
The average Vickers hardness of the fabricated composites as a result of different B4C additions is illustrated in Figure 11. It can be observed that with the addition of 2.5 and 5.0 wt% B4C ceramic particles to Al-20%Mg2 Si composite, the hardness value increased to 79 and 100 HV, respectively in comparison with the base composite (75 HV). The enhancement in the hardness property is attributed to the refinement of primary Mg2 Si particles as well as increasing the content of B4C reinforcements. As observed, the hardness value of Al-20%Mg2 Si-5%B4C hybrid composite is greater than other fabricated composite, which is due to the uniform distribution of B4C particles. The existence of hard  
Figure 10: (**a**) BSE micrograph of Al-20%Mg2 Si-10%B4C hybrid composite, (**b**) SEM image of Al-Mg-B compound, and (**c**) corresponding EDS result of the white particle in (**b**).  
B4C and fine Mg2 Si particles increased the obstacles to the grain boundary sliding [37]. Another factor for the improvement of the hardness of the composite due to B4C addition is increase in the dislocation density as a result of thermal expansion mismatch among the reinforcement and matrix at the particle-matrix interfaces [11]. These factors result in high restrictions to the localized deformation during indentation. However, with further addition of B4C particles to 10 wt%, the hardness value decreased to 84 HV. The reason for the decreasing hardness value is due to the agglomeration of B4C particles in the matrix of the composite (Figure 3d). In addition, as depicted in Figure 4, by increasing the B4C concentration to 10 wt%, the size of primary Mg2 Si particles increased and the density of primary Mg2 Si particles in the composite decreased as well, hence, the number of the Mg2 Si particles to resist the applied load during the hardness test is reduced. Therefore, both B4C agglomeration and low density of primary Mg2 Si particles cause a higher exposure of the soft aluminium matrix to the intender, and as a result, the average hardness of the composite is reduced.
# Convolutional Neural Network (CNN)-Based Frame Synchronization Method
## 4 Simulation Results
During performance evaluation in fading channels, the CNN is not retrained. The same CNN trained with signals under AWGN environments is used and the performance evaluation signals are newly generated. To generate received signals in _fading channels_, the following model is used,  
\[r(t)=c\sum_{k=-\infty}^{\infty}s(k)p(t-kT-t_{0})+w(t), \tag{8}\]  
where \(c\) is the fading coefficient which is Gaussian random variable with variance one. This channel model is suitable for non-line-of-sight terrestrial communication systems. This simulation is performed to confirm whether the proposed technique works well for the received signals different from the training situation. According to the results, the proposed method still 2 dB better than the conventional method. Those results indicate that the proposed CNN operates very flexibly in various channel environments. By improving the frame synchronization performance by 2 dB, the transmitter can reduce the transmit power to increase the battery lifetime, or extend a communication range farther. Figure 6: Learning curves for (**a**) loss and (**b**) training accuracy. To investigate the rationale of why the proposed CNN-based technique outperforms the conventional correlation-based method, we show one snapshot Figure 9 of the correlator output for the case when the correlation-based technique finds the wrong packet arrival time, while the CNN-based method finds it correctly. The ideal correlator output conforms to the normal distribution with a mean value around the packet arrival time due to oversampling effects. The conventional correlation-based frame synchronization method finds just the maximum position ignoring the shape of the distribution of \(y[n]\). However, we can conjecture that the proposed CNN-based frame synchronization method finds the packet arrival time by considering not only the scale but also the shape of the correlation values. Figure 8: False detection probabilities (FDPs) of the proposed CNN-based and the conventional correlation-based synchronization methods in [6,8,10] for \(L=500\) in fading channels. Figure 7: False detection probabilities (FDPs) of the proposed CNN-based and the conventional correlation-based synchronization methods in [6,8,10] for \(L=500\) and \(L=1000\).
# On the Optimal Calculation of the Rice Coding Parameter
## 6 Performance on Different Datasets Using Multi-Parameter Rice Coding
### Improved Compression Factor by Multi-Parameter Rice Coding  
Next, we analyse how much the compression factor can be improved for those batches where sub-sequence partitioning is meaningful.  
In Figure 11, we show the relative difference between the compression factors achieved when the optimal single-parameter and optimal multi-parameter Rice coding approaches are considered. For interval rain and rain intensity, a further compression factor between 20% and 25% can be achieved.  
Figure 10: Number of cases where multi-parameter Rice coding yields shorter codes than the single-parameter Rice coding approach.