esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 DDSGL Experiment Based on DCMS
### Experiment Results and Discussion  
The experimental results are shown in Figure 13. Comparing the uncombined group, mix group 1, and mix group 5 in Figure 13, it is clear that when the leap probability \(k\) and leap space \(S_{nt}\) are small, the effect of DCMS on improving the convergence speed and precision level of roller size in DDSGL is not significant.  
As the leap probability \(k\) and leap space \(S_{nt}\) continue to increase, the convergence speed of the batch diameter variation of the rollers significantly accelerates. Specifically, mix groups 4 and 7 with the fastest convergence speed reach G1 precision about 30 lapping cycles earlier than the unmixed group. The processing time can be shortened by about 50%. Simultaneously, due to more thorough and even comparison among rollers, the precision level of the batch diameter variation of the rollers at this stage is also improved. The batch diameter variation of the roller was increased from 1.15 um in the unmixed group to about 0.8 um in the mix group.  
Upon examining the experimental results, it can be observed that the mix groups 4 (0.87 \(\upmu\)m) and 8 (0.86 \(\upmu\)m), which have the fastest convergence speed, do not exhibit the optimum final precision level for batch diameter variation. The smallest batch diameter variation among the eight experimental groups is achieved by mix group 3, at 0.76 \(\upmu\)m. This finding validates the discussion in Section 3 about the impact of the range of roller cycle counts on the batch diameter variation of rollers. Specifically, when the leap probability \(k\) = 0.3 and the leap space \(S_{n}\) = 30, the batch of rollers can converge to better size consistency and precision at a relatively faster speed.  
Figure 12: Structural modifications based on DCMS, (**a**) DDSGL system, (**b**) Distribution mechanism, (**c**) Dual-channel outlet.  
In summary, when the leap probability \(k\) and leap space \(S_{n}\) reach certain values, CHMS contributes to a faster size convergence speed and a higher precision level of batch diameter variation in the process. In actual production, adjustments can be made according to the production goals. If the aim is to achieve production efficiency at G1 precision, it would be appropriate to increase the leap probability \(k\) and leap space \(S_{n}\). If a higher precision level of batch diameter variation is sought, the leap probability \(k\) and leap space \(S_{n}\) should be selected within a range where the extreme difference in roller cycle counts does not significantly increase.
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 3 The Architectural Model
### The Management and Orchestration (MANO) Plane  
To enable the automated VL encryption and VNF isolation with network redundancy, the architecture suggests having the network intelligence in the control plane, which implies that the Management and Orchestration plane (MANO) is less declarative about the network configuration. Instead of relying on the MANO to instruct the control plane about the locations of the VNFs and their corresponding encryption services, the control plane directly utilizes BGP and Key Management Services in order to configure the network dynamically. This enables the MANO to be less declarative about the network configuration, and enforces the control plane to have imperative services that can be dynamically reconfigured based on physical changes without MANO dependencies.  
We simplify the ETSI reference model and only focus on new top-level services on the orchestration plane. The services include enabling VNF isolation and encryption that are the only parts that are relevant for the control plane components. Hence, the architecture is focused around the control plane where the ETSI MANO components such as the Virtual Infrastructure Manager (VIM), the VNF Manager and the orchestrator are not taken into account. The NFV MANO components are aggregated into one orchestration plane application, which enables a set of service requests, which are discussed in the next section.
# Sources of Extraterrestrial Rare Earth Elements: To the Moon and Beyond
## 5 Lunar Resources
### Other Potential Lunar Resources
#### 5.3.2 Water  
Evidence (indirectly) for water at the lunar poles was provided by the Lunar Prospector, and later supported by the Lunar Crater Observation and Sensing Satellite mission which reported a concentration of 5.6 \(\pm\) 2.9 wt. % (1\(\sigma\)) for water ice in lunar regolith [137; 138; 139]. Considering a water mass of 5.6 wt. %, and a density for the lunar regolith of 1660 kg/m\({}^{3}\), a potential 2900 million tonnes of water exists within the upper 1 m of regolith [24]. Based on current human consumption of 10 billion tonnes of freshwater per day (www.theworldcounts.com/stories/average-daily-water-usage), and 80-100 gallons of water use/person/day [140], 2900 million tonnes (of lunar freshwater) would sustain today's global population for 1 min and 12 s, and one individual for 20.1-26.2 billion years (based on current average use).
# Determining the Optimal Positions of Infill Balise Groups for ERTMS/ETCS Level 1 Applications
### Literature Review
Taking all of this into account, the differences in capacity of no more than 5% and less than 3%, respectively, in favor of level 2 over level 1 indicate potential for level 1, even in larger nodes. Additional research into the effect of the placement of infill balise groups might help to bridge the existing gap of information. The latter two of the aforementioned studies did not aim at optimizing the positions of infill balise groups on a microscopic level and therefore used approximations to efficiently position them for the network segments investigated. It is possible that in some cases, a microscopic optimization of the infill balise groups using an algorithm like the one presented in this paper could have yielded slightly better performance results. [PERSON] [37] presented the concept of using basic probability and the laws of motion to approximate the position of a single infill balise group. He introduced the idea of segmenting the approach zone based on the runtime being equal in each of these segments. The method described by [PERSON] does not take the acceleration phase after receiving infill information into account, but focuses entirely on the braking phase up to the EOA. The achievable acceleration, however, can have significant impact on the operations and capacity after a train had to apply its brakes. No testing with real-world data was performed to verify the model. In summary, this method needs to take additional parameters into account to be applicable for planning purposes considering different train types. A calculation based on a mathematical model using approximated braking and acceleration curves was presented by [PERSON] et al. [38]. To simplify the approach, they used different models for constant as well as variable deceleration and acceleration. The software developed calculates the optimal position of a single infill balise group in addition to one at the distant signal. No real-world train characteristic was used for testing purposes. This proposed method is limited to a constant or parabolic deceleration and acceleration and therefore is not adaptable to closely model the ETCS braking curves. Only a single infill balise group can be positioned and apparently no Key Performance Indicator (KPI) or optimization target to evaluate different settings has been defined. [PERSON] et al. [39] determined the positioning of balises in the context of urban metro systems using a genetic algorithm. However, their aim was to optimize the stopping positions of passenger trains at their respective platforms and not to optimize the operations when approaching a closed main signal about to open.
# Simultaneous Lightwave and Power Transfer for Internet of Things Devices
## 3 System Design and Experimental Methodology
### Detecting UAV Movements  
One of the main applications for a self-powered probe with SLIPT capability is monitoring the environment and activity in harsh or inaccessible locations. For that, the probe needs to be able to be woken up from external sources, such as movement. In this scenario, we demonstrate how to wake up the system with a drone. The device is always set to sleep mode, and only the low-power DAC and OA are enabled to flag any interruption. When a drone flew in a proximity of 50 cm from the sensor, as seen in Figure 8a, the system was  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Distance (m)** & **Gross Data Rate (Mbps)** & **Net Data Rate (Mbps)** \\ \hline
2 & 1.4 & 1.3 \\ \hline
10 & 1.05 & 0.95 \\ \hline
20 & 0.87 & 0.79 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance at different propagation distances.  
Figure 7: Communication characteristics using an OFDM modulation scheme. (**a**) Channel capacity and the bit loading scheme for all used subcarriers. (**b**) Received m-QAM constellations. (**c**) The power loading factor and the SNR for each subcarrier. (**d**) The BER for each subcarrier.  
woken up. The oscilloscope outputs of the IR sensor and the interruption are shown in Figure 8b.  
This experimental test demonstrates the potential use of SLIPT for IoT powering and data collection using UAVs. A UAV can depart from a base station, locate (using global positioning system coordinates, for example), wake up an IoT device, read its information, and power it using LEDs or any other light source. The UAV can also transmit any data while powering the system, for example, sending operation commands or updating software while wireless powering the device's battery.
## 3 System Architecture, Software Services and Deployment
### Cloud-Based Services
#### 3.4.1 Identity Management  
Identity management was designed to allow reliable user authentication services, taking into consideration user equipment, profile or customized security preferences. It supports secure and private mobile device authentication and dedicated web access per user taking into consideration customized profile management selections, all delivered in a way that ensures high levels of privacy regarding the disposition of personal data. The user is capable of generating a new identity only by providing a minimum set of information, which is then stored in an encrypted database. After creating the new identity, the user is able to authenticate with the identity management subsystem and becomes capable of accessing all available cloud services via the OAuth 2.0 protocol using https credentials. Moreover, since the whole prototype framework follows a micro-service-oriented architecture, user records are stored in different, yet interconnected components, which interact with each other through the unique identifier generated by the identity management subsystem. Lastly, the identity management system handles all authentication codes such as passwords or access tokens and is responsible for verifying the authentication roles for each system function.
## 4 Robustness Analysis of the ADN against the CCF
### Robustness Analysis
#### 4.1.1 Distribution of Generators and Robustness of the ADN
\begin{array}{l}R_{PF}(\infty)\ =\ \lim_{N\to\infty}R_{PF}(N)\ =\ \lambda\times f_{P}(\lambda)\\ R_{IF}(\infty)\ =\ \lim_{N\to\infty}R_{IF}(N)\ =\ \gamma\times f_{I}(\gamma)\end{array} \right. \tag{6}\]  
The Equations (5) and (6) are the analytical model to evaluate the robustness of the ADN against the CCF. The relationship between the final expected proportions \(R_{PF}(\infty)\) and \(R_{IF}(\infty)\) of remaining normal operational nodes and the load redistribution is embodied in the mapping \(f_{P}\): \(R\)\(\to\)\(R\) and mapping \(f_{I}\): \(R\)\(\to\)\(R\) (in Appendix B). It can be seen from the conclusion in literature [26] that in the case of distributed generators existing in the PN, \(f_{I}(\gamma)\) and \(f_{P}(\lambda)\) will become larger. According to Equations (5) and (6), the distribution of generators will increase the final expected proportions \(R_{PF}(\infty)\) and \(R_{IF}(\infty)\) of remaining normal operational nodes. Therefore, the first conclusion of our analysis is that the distribution of generators improves the robustness of the ADN against the CCF.
# Retrieval of Biophysical Crop Variables from Multi-Angular Canopy Spectroscopy
## 1 Introduction
This effect has become known as equifinality or _ill-posed problem_ and is dealt with either by restriction of the input range or by inclusion of the n-best performing results rather than just considering the number one fit [24]. For an ideal analysis, communication between sensor and model must be optimal. Since spectral models have been developed in the laboratory with the help of ground-based spectrometers, they basically are of hyperspectral nature. In order to use the models in combination with multispectral data, their spectral resolution normally is toned down using the spectral response functions of the respective instruments. Using hyperspectral data as input allows for making full use of the quasi-contiguous narrowband output of the RTM in forward mode. The retrieval methods tested in this article thus are intended to be applied on hyperspectral data of the Environmental Mapping and Analysis Program (EnMAP). EnMAP is a German spaceborne imaging spectroscopy scientific mission carrying the EnMAP Hyperspectral Imager (HSI) instrument [29]. Currently under development, EnMAP-HSI will deliver data at high spectral resolution of 6.5 nm in the VNIR and 10 nm in the SWIR domain which together cover the full spectral range of 420 to 2450 nm [30]. Competition for actual data is expected to be intense, since the data take capacity of EnMAP is limited and--as of today--only the Italian hyperspectral mission PRISMA [31] may be going to record comparable data by the estimated time of launch in 2019. Repeat cycles of 23 days in quasi-nadir mode will limit the availability of cloud-free scenes [32]. To mitigate this problem, the satellite platform will be capable of a max. \(\pm 30^{\circ}\) across-track tilt, allowing side looks upon the target with revisit times of up to 4 days [33] near earth's equator or even less for latitudes of central Europe, e.g., 2.5 days for Munich, Germany [32]. The effects of this off-nadir pointing for the retrieval of biophysical variables have not been tested in the EnMAP context. Therefore, the objectives of this study are (1) to demonstrate the expected impact of the EnMAP-specific sun-target-sensor-geometry (s-t-s-geometry) on reflectance spectra, (2) to quantify the effect on agriculturally relevant variable retrievals, such as LAI and leaf pigments and (3) to introduce a new hierarchical LUT approach for an optimized retrieval of these parameters.
## 2 Six-Hardpoint Positioning Mechanism and Its Mathematical Model
### Error Model of Six-Hardpoint Positioning Mechanism  
For any robot, error modeling is an important part of kinematic calibration, which requires solving the problems of which errors should be modeled and how to model them. For the six-hardpoint positioning mechanism, factors that affect the pose accuracy of the primary mirror include both geometric errors (such as kinematic parameter errors) and non-geometric errors (such as gravitational deformation and thermal expansion of materials). The kinematic calibration in this study specifically investigates the kinematic parameter errors. The error model is established based on the inverse kinematic Equation (1), which can be rewritten as:  
\[\left|\mathbf{t}+\mathbf{R}\mathbf{a}_{i}-\mathbf{b}_{i}\right|^{2}-{h_{i}}^{2}=0 \tag{12}\]  
where \(\mathbf{a}_{i}\), \(\mathbf{b}_{i}\), and \(h_{i}\) are collectively referred to as kinematic parameters. \(\mathbf{a}_{i}\) and \(\mathbf{b}_{i}\) can be defined as follows:  
\[\mathbf{a}_{i}= \begin{bmatrix}x_{Ai}&y_{Ai}&z_{Ai}\end{bmatrix} ^{T} \tag{13}\]  
\[\mathbf{b}_{i}= \begin{bmatrix}x_{Bi}&y_{Bi}&z_{Bi}\end{bmatrix} ^{T} \tag{14}\]  
where \(i\) = 1, 2,..., 6, with each \(i\) corresponding to a hardpoint. Additionally, there are 7 kinematic parameters related to each hardpoint, of which 3 represent the position of the upper support point, 3 represent the position of the lower support point, and 1 represents the length of the hardpoint. Therefore, the total number of kinematic parameters is 42, and \(\mathbf{C}\) represents the vector composed of all kinematic parameters:  
\[\mathbf{C}= \begin{bmatrix}x_{A1}&y_{A1}&z_{A1}&h_{1}&x_{B1}&y_{B1}&z_{B1}&\cdots&x_ {A6}&y_{A6}&z_{A6}&h_{6}&x_{B6}&y_{B6}&z_{B6}\end{bmatrix} ^{T} \tag{15}\]  
In addition to the above kinematic parameters, Equation (12) also includes the relevant parameter \(\mathbf{q}\), that is the pose of the primary mirror. Therefore, Equation (12) can be rewritten in the following form:  
\[f_{i}(x,y,z,\alpha,\beta,\gamma,x_{Ai},y_{Ai},z_{Ai},h_{i},x_{Bi},y_{Bi},z_{Bi })=0 \tag{16}\]Through complete differentiation, Equation (16) can be transformed into:  
\[ \begin{array}{l}\frac{\partial f_{i}}{\partial x_{Ai}}dx+\frac{\partial f_{i}}{ \partial y}dy+\frac{\partial f_{i}}{\partial z}dz+\frac{\partial f_{i}}{\partial x _{Ri}}dz+\frac{\partial f_{i}}{\partial y_{Ri}}d\beta+\frac{\partial f_{i}}{ \partial y_{Ri}}d\gamma+\frac{\partial f_{i}}{\partial x_{Ai}}dx_{Ai}+\frac{ \partial f_{i}}{\partial y_{Ai}}dy_{Ai}\\ +\frac{\partial f_{i}}{\partial z_{Ai}}dz_{Ai}+\frac{\partial f_{i}}{\partial z_ {Ai}}d\Omega_{i}+\frac{\partial f_{i}}{\partial x_{Ri}}dx_{Bi}+\frac{\partial f _{i}}{\partial y_{Ri}}dy_{Bi}+\frac{\partial f_{i}}{\partial z_{Bi}}dz_{Bi}=0 \end{array}  \tag{17}\]  
which can be expressed in matrix form as follows:  
\[\textbf{J}_{1}d\textbf{q}=\textbf{J}_{2}dC \tag{18}\]  
where:  
\[\textbf{J}_{1}=\left[
# Bi-Objective Power Optimization of Radio Stripe Uplink Communications
## 2 Radio Stripes Network Model
### Payload Transmission and Reception
\tag{12}\]  
The OSLP combining scheme has shown to outperform the MRC approach [11], where  
\[\left\{ \begin{array}{ll}\mathbf{A}_{l}=\mathbf{I}_{K}\\ \mathbf{B}_{l}=\mathbf{\hat{H}}_{l}.\end{array} \right. \tag{13}\]  
Furthermore, it can achieve the same performance as the optimal centralized implementation based on MMSE [9], but in a decentralized fashion and, thus, it allows for a considerable reduction in fronthaul requirements. The combining matrices of the OSLP scheme are [11]  
\[\left\{ \begin{array}{ll}\mathbf{A}_{l}=\mathbf{I}_{K}-\mathbf{T}_{l}\mathbf{ \hat{H}}_{l}\\ \mathbf{B}_{l}=\mathbf{T}_{l},\end{array} \right. \tag{14}\]where  
\[\left\{ \begin{array}{ll}\mathbf{T}_{l}=\mathbf{p}_{l-1}\hat{\mathbf{H}}_{l}^{H} \left(\mathbf{\Sigma}_{l}+\hat{\mathbf{H}}_{l}\mathbf{p}_{l-1}\hat{\mathbf{H}}_ {l}^{H}\right)^{-1}\\ \mathbf{P}_{l-1}=\left(\mathbf{I}_{K}-\mathbf{T}_{l-1}\hat{\mathbf{H}}_{l-1} \right)\mathbf{P}_{l-2}.\end{array} \right. \tag{15}\]  
At the end of the RS sequential processing the final estimate of AP \(L\), \(\hat{\mathbf{s}}_{L}\), is forwarded to the CPU for final decoding. Along the RS network, the achievable SE of UE \(k\) in AP \(l\) is given by [9]  
\[\text{SE}_{kl}=\left(1-\frac{\tau_{p}}{\tau_{c}}\right)\mathbb{E}\{\log_{2}(1 +\text{SINR}_{kl})\} \tag{16}\]  
where the effective signal-to-interference-and-noise ratio (SINR) is [9; 11]  
\[\text{SINR}_{kl}=\frac{p_{k}\left|\boldsymbol{B}_{lk}\hat{\boldsymbol{ \mathcal{H}}}_{kl}\right|^{2}}{\sum_{l=1,j\
eq k}^{K}p_{i}\left|\boldsymbol{B }_{lk}\hat{\boldsymbol{\mathcal{H}}}_{il}\right|^{2}+\boldsymbol{B}_{lk} \mathbf{K}_{l}\boldsymbol{\mathcal{B}}_{lk}}, \tag{17}\]  
while \(\hat{\boldsymbol{\mathcal{H}}}_{kl}=\left[\hat{\mathbf{h}}_{kl}^{H},\cdots, \hat{\mathbf{h}}_{kl}^{H}\right]^{H}\in\mathbb{C}^{Nl}\), \(\boldsymbol{B}_{lk}\in\mathbb{C}^{Nl}\) relates to Equation (11) as \(\hat{\mathbf{B}}_{l}=\left[\boldsymbol{B}_{l1},\cdots,\boldsymbol{B}_{lK} \right]^{T}\), and the term \(\mathbf{K}_{l}=\text{diag}(\hat{\boldsymbol{\Sigma}}_{1},\cdots,\boldsymbol{ \Sigma}_{l})\) is the correlation matrix of the augmented error estimation plus noise term.
# Dynamic SDN Controller Load Balancing
## 5 DCC Two Phase Algorithm
### Phase 1: Initial Clustering  
The aim of initial clustering is to enable the best start that provides the best result for the second phase. There are two possibilities for the initialization. The first possibility is to focus on the distance, that is, seeking an initial clustering which satisfies the distance constraint while the second possibility is to focus on minimizing load difference between clusters.
## 4 Discussion
### Different Responses of NPP/NEP to Climate Change  
The Eurasian continent has been suggested to be a large carbon sink for the world in past decades, especially because of the carbon sequestration capacity of forests of high-latitude ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016); however, dryland ecosystem in this region is less concerned. In 1979-2011, the arid and semi-arid ecosystems of Central Asia experienced significant climate change, which experienced about twice the warming trend than that in Europe ([PERSON] et al., 2016). A decadal drought from 1998 to 2008 was also endured by the drylands. Vegetation activities were considerably affected by climate change, especially warming  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vegetation Type** & **This Study (gC m\({}^{-2}\) yr\({}^{-1}\))** & **Other Studies (gC m\({}^{-2}\) yr\({}^{-1}\))** \\ \hline All types in Central Asia & 281.79 & 218 ([PERSON] et al., 2018) \\ Cropland & 646.25 & 375.27 ([PERSON] et al., 2018), 863.90 ([PERSON] et al., 2018), 341.9 ([PERSON] et al., 2018) \\ Forest & 277.49 & 684 ([PERSON] et al., 2018), 456.8 ([PERSON] et al., 2018) \\ Shrubland & 112.50 & 363.1 ([PERSON] et al., 2018) \\ Grassland & 90.94 & 122.6 ([PERSON] et al., 2018) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Global comparison of net primary productivity (NPP).  
and altered precipitation regime [60; 61; 62]. Trend lines of NPP are similar to variations of precipitation, which indicates that water is the main factor limiting vegetation growth in this region. The findings of this research are in agreement with the study of [PERSON] et al. [4], which discovered a strong positive correlation between vegetation carbon and precipitation.  
As to the different vegetation types, we found that the regions in the arid, semi-arid climate zone with low vegetation cover, such as grassland, showed a stronger correlation between NPP and climate change than high-density vegetation types, such as forest. In addition, we found that cropland NPP was least related to climate change, which may be attributed to the agricultural practices of fertilizer substitution, crop straw, and conservation tillage. Accordingly, improved cropland practices can directly and indirectly mitigate carbon emissions, which benefits the sustainability of croplands. Shrubs in Central Asia, thanks to their root structure, are able to access moisture in deeper soil layers. Previous field experiments reported that desert shrubs in this region had averaged root depth of 3-4 m [4]. Therefore, shrubs are insensitive to changes in temperature and moisture. On the other hand, grasslands, most of which are annual herbs, are more sensitive to climate variability and human activity [14; 15]. Forest is more obviously influenced by nitrogen deposition compared with other vegetation types [63; 64], yet the effects of nitrogen deposition are opposite to rising temperature [65]; therefore, the results would be improved if nitrogen deposition effects were considered.