esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Relative Motion Dynamics Model
### Formation Dynamics Modeling
\begin{split}\bigg{[}\begin{array}{c}\dot{\mathbf{q}}_{a}\\ \dot{\mathbf{p}}_{a}\end{array}\bigg{]}&=\begin{bmatrix}\mathbf{0}_{3}&\mathbf{I}_{3}\\ -\mathbf{I}_{3}&\mathbf{0}_{3}\end{bmatrix}\begin{bmatrix}\frac{\partial\mathbf{H}}{ \partial\mathbf{q}_{a}}\\ \frac{\partial\mathbf{H}}{\partial\mathbf{p}_{a}}\end{bmatrix}+\begin{bmatrix}\mathbf{0}_{ 3}\\ \mathbf{I}_{3}\end{bmatrix}\mathbf{u}\\ &=\left(\mathbf{J}-\mathbf{R}\right)\
abla H(\mathbf{x})+\mathbf{g}\mathbf{u}\end{split}  \tag{19}\]  
where \(\mathbf{J}= \begin{bmatrix}\mathbf{0}_{3}&\mathbf{I}_{3}\\ -\mathbf{I}_{3}&\mathbf{0}_{3}\end{bmatrix} \), \(\mathbf{R}=\mathbf{0}_{6\times 6}\), \(\mathbf{g}= \begin{bmatrix}\mathbf{0}_{3}\\ \mathbf{I}_{3}\end{bmatrix} \). It should be noted that, unless stated otherwise, \(\mathbf{x}\) in the text represents \(\left[\mathbf{q}_{a},\mathbf{p}_{a}\right]^{\mathsf{T}}\). Equation (19) represents the dynamics of EMFF spacecraft relative motion within the port-Hamiltonian framework, where the Hamiltonian function is deduced from the Lagrangian equations. \[ \begin{split} H(\mathbf{x})&=\mathbf{p}_{a}^{\mathrm{T}}\mathbf{ \dot{q}}_{a}-\mathcal{L}\\ &=\frac{1}{2m_{a}}\mathbf{p}_{a}^{\mathrm{T}}\mathbf{p}_{a}-\mathbf{p}_{a}^{ \mathrm{T}}\mathbf{\dot{q}}_{0}-\mathbf{p}_{a}^{\mathrm{T}}\mathbf{S}(\mathbf{q}_{0}+\mathbf{q}_{a })+\mathcal{U}_{S}+\mathcal{U}_{e}\end{split}  \tag{20}\]  
Notably, certain factors may negatively impact the reconfiguration design, including errors in modeling far-field electromagnetic forces and external environmental disturbances. While these factors might be overlooked during the nominal design of reconfiguration trajectories, they are crucial considerations in the design of the formation controller.
## 2 Data and Methods
### The Soil Moisture Active Passive (SMAP) Data  
The SMAP mission is a polar orbiting, remote sensing observatory developed by the NASA primarily for soil moisture mapping. SMAP uses an L-band radar and radiometer with central frequencies of 1.26 GHz and 1.41 GHz, respectively. SMAP primarily detects brightness temperature, which is then converted to soil moisture on land and SSS in the ocean due to the L-band microwave's sensitivity to salinity. SMAP data is independently processed by the NASA Jet Propulsion Laboratory (JPL) and the Remote Sensing System (RSS). Both data providers produce Level-2 (L2) data, which contain data from a single orbit of the satellite (orbital data), and Level-3 (L3) data which are 8-day averages of SMAP salinity data. This study uses SMAP JPL version 5, L2 and L3 data at a spatial resolution of 60 km from 17 January to 2 March 2020 and SMAP RSS version 4, L2 and L3 at two spatial resolutions: 40 km, hereafter referred to as RSS40, and 70 km, hereafter referred to as RSS70 for the same time period.  
The JPL and RSS data products use different retrieval algorithms to generate SSS satellite data. Additional variations include corrections, flags, filters, masks as well as approaches to error and uncertainty estimation. The JPL product includes a land correction algorithm which uses a look up table and 'land-near climatology' to correct brightness temperature. JPL produces a 60 km product and its algorithm retrieves SSS within 35 km from the land whenever sea ice concentration values are less than 3% [27]. Version 4.0 of SMAP RSS mitigates land intrusion using simulated Aquarius and SMAP observations. RSS's method allows retrievals within 30-40 km from land. RSS produces both a 40 and 70 km SSS product; however, salinity retrievals degrade within 500 km of land [22]. The SSS 70 km product is often used as it is significantly less noisy than the 40 km data [22].  
The JPL SSS product has been validated extensively and has shown good accuracy in validations carried out by other studies [28; 29; 30]. [PERSON] [31] validated the SMAP JPL L3 SSS product using in situ data obtained from Argo floats, moored buoys and TSG. The results had a margin of error within 0.2 psu for the zonal band between 40\({}^{\circ}\)N and 40\({}^{\circ}\)S. [PERSON] [13] investigated the accuracy of three L3 salinity products including the SMAP RSS and observed that SMAP RSS was positively biased in the tropics. [PERSON] et al. [32] found in their evaluation of RSS SSS that undetected precipitation and strong winds biases SSS measurements. [PERSON] et al. [30] analyzed the performance of two satellite SSS products (including SMAP RSS) and found that close to the Amazon River RSS SSS products not only captured the full spatial extent of the plume during peak discharge consistently, but also the salinity gradients [30].
## Appendix A Values of Constants Used in the Model  
This appendix compiles the values of the constants used in the model, organized as a function of the subsystems in which they are located.  
* PV subsystem
* PV module: Solartech Power SPM210P (four modules in series and variable number of arrays as a function of the participation factors)
* \(C_{capacitor}=100\)\(\mu\)F
* \(L_{inductor}=10\) mH
* D-controlled boost converter with default Simulink settings
* PI controller: \(P=0.03\); \(I=1\)
* Droup constant of active power reserve adaptation = 0.05
* Initial reserve level = 0.2
* VSWT system
* \(P_{base}=P_{t,base}=P_{g,base}=1.5\) MW, \(v_{nom}=12\) m/s, \(\omega_{t,base}=1.644\) rad/s, \(\omega_{g,base}=157.08\) rad/s, \(f=50\) Hz
* Values for the model blocks taken from [36]
* Speed governor PI controller: \(P=3\); \(I=80\)
* Pitch governor P controller: \(P=500\)
* Inertial control [24; 39]: \(K_{opt}=0.4225\), \(k_{vir}=8\), \(W_{vir}=0.2\), \(T_{x0}=10\) s, \(T_{lp}=100\) ms, \(H_{WT}=5.29\) s
* Steam turbine with one reheat [24; 32]
* \(H_{\alpha q}=2.88\) s
* Droop constant = 0.05
* \(\tau_{g}=0.2\)
* \(F_{HP}=0.3\)
* \(T_{RH}=5\) s
* \(T_{CH}=0.3\) s
# Entangled Frequency-Tunable Microwave Photons in a Superconducting Circuit
## 4 Numerical Results
### Frequency-Tunable Entangled Photon Pairs  
Due to the tunability of SQUID, which acts as a tunable inductance in the frequency-tunable TLR, \(\omega_{a}(\Phi_{\mathrm{dc}})\) and \(\omega_{b}(\Phi_{\mathrm{dc}})\) can be adjusted by an external dc flux bias in situ. By implementing proper pump power according to the pump frequency, one can make the emitted pairwise photons reach a steady entanglement at each working point.  
We record \(\mathcal{N}\) as a standard to evaluate the degree of entanglement as well as the frequencies of the emitted two-mode signals with corresponding pump frequency \(\omega_{p}(\Phi_{\mathrm{dc}})=\omega_{a}(\Phi_{\mathrm{dc}})+\omega_{b}( \Phi_{\mathrm{dc}})\). As shown in Figure 6c,d, pairwise photons with varied frequencies at different working points are generated in the range of 4 GHz. The saturated entanglement is stable around \(\mathcal{N}=0.5\). Note that the equivalent inductance of SQUID has larger Kerr-nonlinearity when it works in the vicinity of \((k+0.5)\Phi_{0}\) (\(k\in\mathbb{Z}\)) [28]. In addition, the strong pump field may induce a shift in resonant frequencies [54; 55; 56; 33]. These two factors will modify the frequencies of the two modes and are worthy of attention in the tuning of the source. In low temperatures such as 10 mK, the effect of thermal excitation on the properties of our circuit can be neglected, which is proved by our numerical calculations. The effect of decoherence is dominated by the decay rates \(\kappa_{a}\) and \(\kappa_{b}\) of the system.
# Chemical Composition and Antioxidant Activity of _Asteraceae_ Family Plants
## 4 Discussion
### Common Dandelion (Taraxacum officinal [PERSON].)  
The literature contains mainly data on the proximate composition of leaves. [PERSON] et al. [52] found higher values of crude ash (12.28 g/100 g DM) and carbohydrates (58.69 g/100 g DM) in dandelion leaves than were found in our study. At the same time, the protein content (15.48 g/100 g DM) was lower [53]. Flowers, if used in food preparation, are usually selected for their appearance and/or taste. However, very rarely treated as a source of high nutritional value, including valuable dietary fiber or protein [54]. Therefore, due to the rich source of these nutrients, flowers can also be included in the daily diet. As a source of dietary fiber, they can act as a prebiotic for intestinal bacteria.  
Reports on the content of polyphenols are very diverse and concern mainly dandelion herbs, less often its roots and leaves. The research of [PERSON] et al. [55] showed the content of 15.50 mg GAE/g DM of polyphenolic compounds in the dandelion herb. [PERSON] et al. [56] obtained similar results. In the dandelion leaves, the total phenolic content was 15.3 mg GAE/g DM. [PERSON] et al. [12] found 12.6 mg GAE/100 g DM of polyphenols in the dandelion root. [PERSON] et al. [40] determined the antioxidant activity of dandelion leaves using the FRAP method and obtained the result of 4256 uM/L. Detailed data on the antioxidant activity of the dandelion root was obtained by [PERSON] et al. [12]. The activity of the raw material was determined by three methods: ABTS, DPPH and FRAP. The antioxidant activity of the dandelion root investigated using the ABTS method was 1.76 uM Trolox/100 g DM, by the DPPH method 213 uM Trolox/100 g DM, while the FRAP method was 15.9 uM Trolox/100 g DM.
## 3 Aim, Materials, and Methods
### Integrated Intelligent Perception Mode of \"Sky Air and Ground\"
#### 4.1.1 Sky Sat Satellite Communication System  
The Sky Sat satellite communication system comprises a diverse range of satellites, encompassing various domains such as communication, Beidou navigation, remote sensing, and Earth observation. In the event of natural disasters like earthquakes, floods, and landslides, the Sky Sat satellite communication system can promptly initiate an emergency response mechanism by deploying relevant satellites for the real-time monitoring of the affected areas. By assimilating data from multiple sources, it enables accurate assessment of the disaster's scale, impact extent, and potential casualties. Moreover, this system can also relay crucial disaster information to unmanned aerial vehicles (UAVs) and other sensing devices through network transmission. This approach significantly enhances decision-makers' comprehension regarding the comprehensive nature and precision of on-ground conditions in disaster-stricken regions while providing a scientific basis for effective rescue operations and post-disaster reconstruction efforts. To summarize its applications thus far: the Sky Sat satellite communication system has been employed in fields including natural disaster monitoring, environmental surveillance, as well as public safety measures. Consequently, rural emergency management should draw insights from these cases and experiences to expedite the development of natural disaster information systems.
# Private and Secure Distribution of Targeted Advertisements to Mobile Phones
## 3 Related Work
### Classification of Available Systems
#### 3.1.1 Trusted Proxy-Based Anonymity  
The simplest method of achieving anonymity is by introducing some form of trusted third party that acts as a proxy between the user and Ad-Network. The role of the proxy is to mask the identity of the user by forwarding his/her requests after replacing any identifying information with a temporary identifier. The Ad-Network uses this temporary identifier to reply to the proxy with relevant adverts, which are then conveyed back to the user.  
In order to further increase privacy, public key encryption can be used to encrypt requests and adverts. When paired with cryptography, the proxy is aware of the identity of the user that is sending the requests, but cannot see the content of the requests nor the corresponding ads. In turn, the Ad-Network or any other entity that distributes ads can decrypt the encrypted requests, but is not aware of the user's true identity, as it is masked by the proxy.
## 5 Performance Evaluation
### Simulation Results
#### 5.2.1 Results for NSFNET
The CP method tended to use cores with assigned priorities in order, preventing inter-core crosstalk. Therefore, as the number of cores increased, the crosstalk reduction effect became more significant. On the other hand, the proposed method focused on reducing the blocking probability by flexibly determining the cores to be used based on the weight parameters, even though some inter-core crosstalk occurred. However, the amount of inter-core crosstalk can be considered sufficiently small in this case. Note that with an appropriate choice of the weight parameters, the proposed method is expected to achieve performance comparable to that of the CP method. Figure 9(a),b show the blocking probability of lightpath establishment and the average number of inter-core crosstalk occurrences, respectively, as a function of the value of \(\gamma\) in the proposed method in NSFNET. As shown in Figure 9(a), the blocking probability  
Figure 8: Results in NSFNET (\(|\mathcal{C}|=7\)). increased with the value of \(\gamma\), regardless of the number \(|\mathcal{C}|\) of cores. In contrast, the number of inter-core crosstalk occurrences decreased with the increase in the value of \(\gamma\). There exists a trade-off between the blocking probability and the number of inter-core crosstalk occurrences with respect to the value of \(\gamma\). Therefore, it is important to decrease the value of \(\gamma\) to ensure that any arbitrary crosstalk threshold can be satisfied, taking this trade-off into account. If the crosstalk level is sufficiently low, selecting \(\gamma=0\) yields the best performance, as it minimizes the blocking probability of lightpath establishment. The acceptable crosstalk level depends on the assumed environments; thus, we need to select the appropriate value of \(\gamma\) accordingly. A detailed discussion on the relationship between the value of \(\gamma\) and the crosstalk threshold is left for future work.
## 6 Conclusions  
We presented the assessment of a photonic integrated WDM optical cross-connect as one of the main building blocks for interconnecting network elements, as well as computing and storage resources implemented in an optical metro node architecture. The photonic WDM cross-connect node based on semiconductor optical amplifiers (SOA) allows switching data signals in wavelength, space, and time for fully exploiting statistical multiplexing. The assessment of a 4 \(\times\) 4 photonic WDM cross-connect in terms of switching speed, photonic integrated amplification for loss-less operation, and gain equalization has been reported. The experimental results confirm the potential lossless operation, large optical power dynamic range, low cross-talk <\(-\)30 dB, and multicasting operation. Compensation of the losses is a good indication that the modular architecture could scale to a larger number of ports. Moreover, the assessment of the cross-connect switch with multiple WDM channels and diverse modulation formats indicate dynamically switching within a few nanoseconds the WDM data packets and error-free operation with less than a 2 dB power penalty for single channel, as well as WDM input operation, for multiple 10/20/40 Gb/s NRZ-OOK, 20 Gb/s PAM4, and data-rate adaptive DMT traffic.  
Considering the practical implementation, due to the identical modular structure, the scalability of the WDM cross-connect switch is mainly determined by possible AWG crosstalk degradation with the increase of the number of ports and by the broadcast and select optical switch. On the AWG crosstalk, it has been demonstrated the potential to have a large (>64) number of ports with very slight degradation of the crosstalk. The splitting loss caused by the broadcasting stage can be compensated by the SOA gates, guaranteeing loss-less operation and sufficient power level for the receiver side, the noise added by the SOA will lead to an OSNR degradation. Therefore, the main limiting factor of scaling out the port-count beyond 64 ports is the OSNR degradation due to the splitting loss experienced by the optical data.  
Figure 15: (**a**) Optimal input power; (**b**) bit allocation per subcarrier; and (**c**) maximum data-rate with different received optical powers for DMT traffic.  
**Acknowledgments:** The authors would like to thank H2020 METRO-HAUL project (grant number 761727) for supporting this work, and JePPIX for the chip fabrication.  
**Author Contributions:** [PERSON] conceived and designed the PIC WDM cross-connect and wrote the paper. [PERSON], [PERSON] and [PERSON] performed the experiments and analyzed the data with [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Research Progress on Router Devices for the OAM Optical Communication
## 5 OAM Channel Hopping
The inactive state is assigned to the remaining (N-I) OAM modes. During the operation of the Double-Serial Mode-Hopping (DSMH), index selector B selectively activates one OAM mode as the second serial hop based on the index information. The activation pattern of OAM modes follows a consistent sequence for both transmitters and receivers while appearing random to potential attackers. Extensive numerical analysis demonstrates the effectiveness of their proposed schemes in achieving low Average Bit Error Rates (ABER) and significantly increasing Spectral Efficiency (SE) within a narrowband. Specifically, the ABERs of the proposed IM-MH and IM-DSMH schemes are approximately 25% and 10% of the traditional schemes, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Equipment**} & \multirow{2}{*}{**Hopping Type**} & \multicolumn{2}{c}{**Strengths/**} & \multicolumn{1}{c}{**Transmission**} & \multirow{2}{*}{**Ref.**} \\  & & & & & **Weaknesses** & & \\ \hline \multirow{3}{*}{2015} & Mode hopping & Digital & \multirow{3}{*}{OAM mode} & Fast Channel & The measured power & \multirow{3}{*}{[145]} \\  & controller & micromirror & & hopping/Additional & penalties are below & 5.3 dB. & \\ \hline \multirow{3}{*}{2018} & Orthogonality & PNG, BPF, LPF, & OAM mode, & \multirow{3}{*}{Low BER/} & The BER is much & \multirow{3}{*}{[146]} \\  & of OAM modes & integrator & frequency & Low integration & lower than the & \\ \hline \multirow{3}{*}{2022} & IM-MH scheme & Signal modulator, & \multirow{3}{*}{mode hopping} & Low ABERs and & The ABER is around & \multirow{3}{*}{[147]} \\  & & index selector, & & Increase the SE/ & 25\% of the traditional & \\ \cline{1-1}  & & UCA & & - & scheme & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of channel hopping routers used for the OAM-based optical communications (PNG, pseudorandom noise sequence generator; BPF, band pass filter; LPF, low pass filter; UCA, uniform circular array). At present, most OAM-RD which can realize the channel switching function realizes the strong anti-interference ability of wireless communication by mode hopping or frequency hopping [148; 149]. However, only one OAM mode/band is excited to load and transmit line information per hop, resulting in low-capacity problems caused by low OAM mode or spectral interest rate. This problem is at odds with the increasing data rate requirements of wireless communications. Therefore, how to improve the OAM-RD which can realize channel hopping is a priority in future research direction. Table 5 summarizes the works reported to date for the channel hopping routers OAM-based optical communication.
# Relationship between CO2 Fertilization Effects, and Stand Age, Stand Type, and Site Conditions
## 3 Methods
### Analysis of the Variation of \(\beta_{GPP}\) with Stand Age  
We used coniferous forests (CFs) and broad-leaved forests (BFs) with a stand age of 1-100 years as research objects. We matched the annual \(\beta_{GPP}\) of all pixels with stand age, and we considered the average \(\beta_{GPP}\) at the same stand age to be the final estimate of the \(\beta_{GPP}\) for that stand age (Equation (7)). Finally, we use nonlinear regression to assess the relationship between \(\beta_{GPP}\) and stand age (Equation (8)). We observed that the relationship between \(\beta_{GPP}\) and stand age was similar among the different remote sensing products used within the same stand type. As a result, in order to more clearly compare the differences between different stand types, we utilized the average of the \(\beta_{GPP}\) derived from these two remote sensing datasets to construct the relationship between \(\beta_{GPP}\) and stand age for each stand type.  
\[\beta_{y}^{L}=\frac{\sum_{i=1}^{n}\beta_{iy}^{L}}{n} \tag{7}\]  
\[\beta_{GPP}=a\left[1+\frac{b\left(\frac{gGC}{c}\right)^{d}-1}{e^{(\frac{gGC }{c})}}\right] \tag{8}\]  
where in Equation (7), \(\beta_{y}^{L}\) represents the CFE when the stand age is \(y\) under \(L\) stand type, \(\beta_{iy}^{L}\) represents the CFE intensity of the \(i\)th pixel (\(i=1\ldots n\)) under \(L\) stand type when the stand age is \(y\), \(n\) represents the number of pixels corresponding to \(y\) stand age. In Equation (8), \(\beta_{GPP}\) represents the change in GPP for every 100 ppm increase in atmospheric CO\({}_{2}\) concentration; and \(a\), \(b\), \(c\), and \(d\) are nonlinear regression model parameters, where parameter \(a\) determines the general magnitude of \(\beta_{GPP}\), and \(b\), \(c\), and \(d\) are parameters determining the rate of \(\beta_{GPP}\) changing with stand age. The maximum value of \(\beta_{GPP}\) increases with the increase in parameters \(b\) and \(d\), the stand age at which the maximum value is reached increases with the increase in parameters \(c\) and \(d\).