esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Discussion
### Micro-Vibration Test and Coupling Analysis
To address this problem, we conduct the micro-vibration test while operating the CMGs and cryocoolers independently to analyze the coupling of micro-vibration responses under the steady-state observation missions. Figure 16: Comparison of micro-vibration results measured by different test systems: (**a**) image point movement; (**b**) LOS jitter around the X-axis; (**c**) LOS jitter around the Y-axis. Figure 17: LOS jitter of the steady-state observation conditions. Since the environmental impact on the micro-vibration cannot be ignored at low frequencies, this paper only examines the LOS jitters above 5 Hz, as shown in Figure 18.  
As shown in Figure 18, the peak frequencies of the LOS jitter caused by CMGs and cryocoolers are approximately within \(5\sim 100\) Hz. Since the LOS jitter caused by the same disturbance is definite in the frequency domain, a linear superposition of the micro-vibration response in the frequency domain was obtained through Equation (16) to compare with the collaborative test, as shown in Figure 19.  
\[\left\{ \begin{array}{l}LS_{x}=\sum FFT(J_{xi},N_{xi})/L_{xi}\\ LS_{y}=\sum FFT(J_{yi},N_{yi})/L_{yi}\end{array} \right. \tag{16}\]  
where \(LS_{x}\) and \(LS_{y}\) are the superposition results of the LOS jitter around the X-axis and the Y-axis in the frequency domain; \(J_{xi}\) and \(J_{yi}\) express the LOS jitter measured in the time domain, where \(i\) is the symbol that varies with CMGs or cryocoolers; \(L_{xi}\) and \(L_{yi}\) are the data lengths of \(J_{xi}\) and \(J_{yi}\); and \(N_{xi}=2^{network(L_{xi})}\) and \(N_{yi}=2^{network(L_{yi})}\), where \(nextpow(\cdot)\) is used to calculate the smallest power of 2 that is greater than or equal to \(L_{xi}\) or \(L_{yi}\). As shown in Figure 19, the results of the collaborative test are lower than the linear superposition, which means that the coupling effect may result in LOS jitter reduction. Table 2 lists the typical responses of the LOS jitter peaks. It is not difficult to see that the LOS jitter of the linear superposition is higher than the collaborative test. For the LOS jitter around the X-axis, the results superimposed linearly are about eight times and four  
Figure 19: Coupling of micro-vibrations between CMGs and cryocoolers: (**a**) LOS jitter around the X-axis; (**b**) LOS jitter around the Y-axis. Figure 18: Micro-vibrations caused by CMGs and cryocoolers: (**a**) LOS jitter around the X-axis; (**b**) LOS jitter around the Y-axis. times the collaborative test at 8.74 Hz and 16.53 Hz. And for the LOS jitter around the Y-axis, the results superimposed linearly are more than four times the collaborative test at 8.05 Hz and 16.53 Hz. Notably, the LOS jitters collected in the collaborative test still satisfy the sub-micro-radian pointing accuracy requirement, while the results of linear superposition have been far more than 0.1 \(\upmu\)rad. Therefore, it is necessary to adopt a collaborative test, rather than linear superposition, to evaluate the LOS jitter of the optical payload with sub-micro-radian pointing accuracy before launch.
## 3 Discussion
### Jaborosa reflexa: Transition to Generalized Pollination in the Andes  
Not all species of _Jaborosa_ have specialized pollination systems relevant to specific syndromes. Among the five _Jaborosa_ species sampled from the Andean clade, _J. reflexa_ is the only species with a generalized pollination system, producing nearly glabrous flowers that are polymorphic for color and are visited by small flies (Syrphidae), medium size flies (Sacrophagidae) and bees (Halictidae) [56]. Accordingly, flowers of _J. reflexa_ produce a scent profile containing more conventional aromatic and isoprenoid volatiles common to flowering plants with diverse pollination systems [1]. These compounds are similar to those of the hawkmoth-pollinated _Jaborosa_ species, although they are emitted in much lower quantities in _J. reflexxa_ (Tables S2 and S3). Furthermore, _J. reflexxa_ samples in our survey were collected from three different populations, all of which produce linalool and (E)-DMNT (e.g., Rio Chico) but which otherwise varied in the presence of methyl benzoate and farnesenes (El Trebol) or long-chain alkenals and benzyl benzoate (La Leona).  
Ancestral state reconstruction suggests that _J. reflexxa_ may have diverged recently from a saprophilous fly-pollinated ancestor of the Andean clade [55]. Although the origins of this divergence are unclear, extant populations of _J. reflexxa_ overlap in both ENM dimensions and actual geographic ranges with _J. laciniata_ and _J. leucotricha_ (Figure S4), which are pollinated by distinct guilds of flies [56]. The generalized pollination of _J. reflexxa_ may facilitate coexistence with other _Jabrosa_ species through reduced competition for pollininators. Results of our previous studies with other _Jabrosa_ species make it clear that the experimental restoration of oligosulfides (DMDS, DMTS) to flowers of _J. reflexxa_ would instantly attract carrion flies to augmented flowers in their Andean habitat (see [58]).  
The closest relative to _J. reflexxa_ in our study, _J. laciniata_, also emits some conventional volatiles (e.g., linalool, methyl benzoate), which we predict may be shared with a sister species (_Jabrosa kurtzii_ Hunz. and Barboza) whose floral biology also suggests generalized pollination [55; 56]. If _J. reflexxa_ represents a transition from a fly-pollinated ancestry to a more generalized pollination system, we hypothesize that this might have been accomplished through the loss of glandular trichomes responsible for emissions of S- and/or N-bearing volatiles in a putatively fly-pollinated ancestor. This inference is based on the observation that S-volatiles are produced by trichome-covered immature flowers and opening buds of _J. rotacea_[57] and _J. sativa_ (Table S4), whereas the flowers of _J. reflexxa_ and the closely related _J. kurtzii_ are nearly glabrous (see Figure 3 in [55]).
# Comparison of Basic Notch Filters for Semiconductor Optical Amplifier Pattern Effect Mitigation
## 7 Conclusions  
In conclusion, we have presented a theoretical analysis and benchmarking of two basic optical notch filters employed to compensate for the pattern effect that manifests in an SOA due to the irregular perturbation of the SOA gain dynamics by a directly-amplified data-modulated optical signal. After following a rational procedure based on the satisfaction of signal amplitude equalization conditions, we extracted values of the critical operating parameters, which enable a fair comparison of these filters through evaluating them against appropriately-adopted figures-of-merit. In this manner, we have specified the points where the considered filters perform better due to their different principles of operation and associated spectral responses. The obtained results denote that, with the exception of larger amplitude modulation, which however is well compensated by the other comparative realized improvements, the MRR outperforms the ODI in most employed metrics. This conclusion is further supported by the comparison made between the filters from a functional and technical side. In fact, the MRR exhibits several attractive key characteristics, which, along with its capability to confront the SOA pattern effect, allows it to favorably compete against the well-established ODI. This fact constructively broadens the technological options that are available for addressing the target problem. Furthermore, it indicates that, since there is no absolute criterion for preferring one filter to the other, making a selection should be done in such a way so that it best suits the particular needs of a light wave link or network where SOA direct amplification with a suppressed pattern effect is essential. To this end, each filter should judiciously be exploited while making the necessary trade-offs according to its inherent performance advantages and disadvantages.  
[PERSON] conducted the simulation and interpreted the obtained results; [PERSON] conceived the initial idea and wrote the paper; [PERSON] reviewed the results and the manuscript.  
The authors declare no conflict of interest.
## 3 Architecture and Problem Formulation
### Problem Formulation  
An energy efficient uplink scheduling and transmit power control mechanism can be obtained using the described system model for both the direct and relayed communication design for mMTC and uRLLC nodes. For a hybrid HTC/MTC co-existing design this has been formulated as:  
\[max_{c_{k}\beta_{k}}L_{n}^{sil} \tag{15}\]  
subject to:  
\[\begin{array}{l}\text{C1}:\sum_{k\ \in\ K}c_{k}\ \leq\ |\text{C}|,\\ \text{C2}:\ D_{k}\ \leq TBS(c_{k},\delta_{k}),\forall k\ \in\ A\\ \text{C3}:\ P_{UL}(c_{k},\delta_{k})\ \leq P_{max},\forall k\ \in\ A\\ \text{C4}:\ \delta k\ \in\ \{0,\dots,\ 33|\};\ c_{k}\in\{1,\dots,|\text{C}|\},\ \forall k\ \in\ A\end{array}\]  
where \(K\) is the set of schedulable devices, \(L_{n}^{sil}\) depends on the individual battery lifetimes for mMTC and uRLLC devices. \(|\text{C}|\) is the set of available PRBPs, e.g., 110 when considering an LTE system with a bandwidth of 20 MHz. This battery lifetime maximization problem for coexisting mMTC and HTC devices is modeled into an energy efficient power control problem for MTC devices. This leads to an improved utilization of available energy in battery reserves of MTC devices by consideration of interference and QoS requirements of both MTC and HTC devices. This paper proposes an approach based on differential and mean field game theory to solve this optimization problem, resulting in mean field-based power control for MTCDs.
# Study on Preparation and Rheological Properties of 3D Printed Pre-Foaming Concrete
## 5 Conclusions  
This study is aimed to explore the effect of surfactant content on the rheological properties of 3D printed pre-foaming concrete. The changes of mechanical properties, dry density, thermal conductivity and pore structure were measured and discussed. It is expected to provide a valuable reference for the practical application of lightweight pre-foaming concrete in 3D printing. The test and analysis conclusions are as follows:The addition of 0.1 wt.% HPMC to 3 DFCs increases the static yield stress and apparent viscosity (especially at low shear rate), thus enhancing the buildability. Although the fluidity of 3 DFCs is reduced, its loose and porous internal structure reduces the resistance to pumping, which is suitable for printability.
* The buildability of 3 DFCs containing various dosages of surfactant is evaluated via the static yield stress development, apparent viscosity and viscosity recovery test. Result demonstrates that the incorporation of surfactants reduces the static yield stress and apparent viscosity, but the viscosity recovery rate of all 3 DFCs is similar.
* The MIP test results show that the number of macropores (10-100 \(\upmu\)m) and total pores in hardened foam mortar increase obviously with the increase of surfactant. In addition, SEM images show that when the foam content in 3 DFCs increases, the pore size and the number of large pores (100-500 \(\upmu\)m) in 3 DFCs will also increase.
* The presence of surfactant can effectively reduce the volume density of 3 DFCs from 2211 to 1159 kg/m\({}^{3}\) but also weaken the compressive strength of 3 DFCs. This is due to the generation of more pores with the addition of surfactant and consequently to the decrease of compressive strength and density. Moreover, the thermal conductivity of 3 DFCs decreases slightly from 0.288 to 0.255 W/(m-K), with the gradual increase of surfactant dosage, which is also related to the development of pore structure.  
Y.G.: Scheme design, Data curation, Writing--original draft, Visualization, Investigation. S.H.: Provide inspiration, Funding acquisition, Supervision. H.Y.: Assist in the experiment. All authors have read and agreed to the published version of the manuscript.  
The authors are grateful to the support of this work by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). We are also grateful for the financial support of Research Funds of Jiangsu Hydraulic Research Institute (2022Z015), Suzhou Science and Technology Program Project (SS202117) and Zhangjiagang Industry-University-Research pre-Research Fund Project (ZKCXY2156).  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 2 Study Area and Data Used
### Landslide Inventory  
Landslide locations were identified using 32 aerial photographs of the study area, taken after the occurrence of the landslides. These aerial photographs were taken by a digital mapping camera with a spatial resolution of 10 cm. The orthorectified photographs were produced using the Leica Photogrammetry Suite (LPS) mounted on ERDAS Imagine 2011 (Erdas, Inc., Norcross, GA, United States). Landslide locations were digitized by visual interpretation using ArcGIS 10.2 (ESRI, Inc., Redlands, CA, USA). Among the digitized landslide locations, landslide locations belonging to rupture zones were converted to point data using a centroid technique. The point data representing the landslide locations were converted to a pixel format, with resolution of 10 m. From the 140 identified landslides, 42 (30%) were reserved to validate the model, after 98 (70%) had been chosen at random for model training. Additionally, non-landslide pixels were selected randomly from the non-landslide area: 98 non-landslide pixels were used for the training dataset, and 42 non-landslide pixels were used to build the validation dataset. This generating and splitting process was performed repeatedly  
Figure 1: Location of study area (**a**) and landslide inventory map with hill shading (**b**).  
more than 10 times. Finally, the combination utilized was found through the area under the receiver operating characteristic (ROC) curve (AUC) method.
## 2 Materials and Methods  
### Study Area and Data  
The model development was conducted using data from the conterminous United States because of the availability of field data sets for the validation of some of the model results.  
The most important data sets in this study are the Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data sets, gridded rainfall, reference ET (ETo), and gridded soil water holding capacity (WHC). In addition, latent heat flux data from AmeriFlux towers were used to validate the results. The average weekly maximum NDVI values (1989-2004) were acquired from the U.S. Geological Survey (USGS) Earth Resources Observation and Science Center (EROS).[15].  
Precipitation is a key driver of the water balance model. Daily gridded rainfall data at 5-km spatial resolution was acquired from the National Weather Service (NWS) for 2005 and 2006. The NWS precipitation data is produced as a merged product between WSR-88D NEXRAD (Next Generation Radar) and ground rainfall gauge reports ([[http://www.srh.noaa.gov/rfcshare/precip_about.php](http://www.srh.noaa.gov/rfcshare/precip_about.php)]([http://www.srh.noaa.gov/rfcshare/precip_about.php](http://www.srh.noaa.gov/rfcshare/precip_about.php))). WHC, in mm per meter depth of soil, is derived from the State Soil Geographic Database (STATSGO) ([[http://www.ncgc.nrcs.usda.gov/products/data](http://www.ncgc.nrcs.usda.gov/products/data)]([http://www.ncgc.nrcs.usda.gov/products/data](http://www.ncgc.nrcs.usda.gov/products/data)) sets/statsgo/) for the United States. Daily ETo is produced at USGS/EROS from 6-hourly meteorological data from the Global Data Assimilation System (GDAS) using the standardized Penman-Monteith equation [2] for the globe [16]. The ETo was scaled down from 1.0 degree to 0.1 degree using the method described in [PERSON] et al. [4]. Model validation data sets on latent heat flux and point-rainfall were downloaded from the AmeriFlux towers sites ([[http://public.ornl.gov/ameriflux/index.html](http://public.ornl.gov/ameriflux/index.html)]([http://public.ornl.gov/ameriflux/index.html](http://public.ornl.gov/ameriflux/index.html))). Latent heat flux from the AmeriFlux towers is measured using an eddy correlation method. Daily raw 30-minute latent heat flux data (w/m\({}^{2}\)) downloaded from the Web site were converted into daily ET equivalents using the latent heat of vaporization of water at 2.45 MJ/kg, which is equivalent to using 2.45 MJ of energy to evaporate 0.001 m\({}^{3}\) of water (1 mm over 1 m\({}^{2}\), assuming 1000 kg/m\({}^{3}\) for the density of water). Thus, 1 mm/day of ETa requires an average of 28.356 (2.45 * 10\({}^{6}\) / (24 * 3600)) w/m\({}^{2}\) of power throughout the day. Thus, daily average latent heat flux data in w/m\({}^{2}\) from the AmeriFlux towers were converted to ETa equivalents in mm/day using a conversion factor of 0.03525 (1/28.356). In addition, the Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Continuous Field (VCF) data was used as an exploratory data set for evaluating the percent vegetation cover in each pixel [17].
## 3 Control Design of Multi-UAVs
### Containment Control  
The containment control model is introduced in this section.  
Suppose we have an N vehicles dynamic in 2D as follows:  
\[\dot{x}_{i}(t)=A_{uav}x_{i}(t)+B_{uav}u_{i}(t)\] \[y_{i}(t)=C_{uav}x_{i}(t)\ \ \ i=1,2...N\text{ and }x_{i}(t)\in R^{2} \tag{26}\]  
where  
\[A_{uav}=diagonal(\begin{bmatrix}0&1\\ a_{21}^{1}&a_{22}^{1}\end{bmatrix})\cdot\cdot\cdot\begin{bmatrix}0&1\\ a_{21}^{n}&a_{22}^{n}\end{bmatrix}),\] \[B_{uav}=I_{n}\otimes\begin{bmatrix}0\\ 1\end{bmatrix},x_{i}(t)=\begin{bmatrix}x_{p}\\ x_{p}\end{bmatrix}\text{and }\dot{x}_{i}(t)=\begin{bmatrix}x_{p}\\ x_{a}\end{bmatrix}\]  
Now Equation (26) can be simplified to:  
\[\begin{bmatrix}x_{v}\\ x_{a}\end{bmatrix}=\begin{bmatrix}0&1\\ a_{21}&a_{22}\end{bmatrix}\begin{bmatrix}x_{p}\\ x_{v}\end{bmatrix}+\begin{bmatrix}0\\ 1\end{bmatrix}u_{i} \tag{27}\]  
**Definition 1**.: _\"A formation in Figure 2 is a vector \(h=h_{p}\otimes\begin{bmatrix}1\\ 0\end{bmatrix}\in R^{2N}\). The N agents are in formation \(h\) if there are vectors \(q,w\in R^{N}\) such that \((x_{p})_{i}-(h_{p})_{i}=q,(x_{v})_{i}=w,i=1,...,N\) where subscript \(p\) (position) and subscript \(v\) (velocity) are components of \(x_{i}\)\" [35]._  
Furthermore, the error between agents is determined from an average relative movement of adjacent agents as follows:  
\[e_{i}=(x_{i}-h_{i})-\frac{1}{|j_{i}|}\sum_{j\in j_{i}}(x_{j}-h_{j})\ \ \ i=1, \ldots,N. \tag{28}\]  
Figure 2: Containment of multi agents.  
We can derive the output vector of all vehicles equations in a single system by using the Laplacian matrix, \(L_{\text{C}}\) as follows:  
\[\dot{x} =Ax+Bu \tag{29}\] \[e =L(x-h)\]  
with \(A=I_{N}\otimes A_{uw}\), \(B=I_{N}\otimes B_{uw}\)_and_\(L=L_{\text{G}}\otimes I_{2n}\).  
Our goal is to calculate feedback matrix \(F\), a matrix of feedback that lead the vehicle to a formation \(h\).  
\[\dot{x}=Ax+BFL(x-h) \tag{30}\]  
By substituting \(A,B\) and \(F=I_{N}\otimes F_{uw}\) we obtain:  
\[\dot{x}(t)=I_{N}\otimes A_{uw}x(t)+L_{\text{G}}\otimes B_{uw}F_{uw}(x(t)-h) \tag{31}\]  
The result would be the corresponding forms of matrix, \(A,B\), and \(F\) as follows:  
\[(U^{-1}\otimes I_{2n})(A+BFL)(U\otimes I_{2n})=I_{N}\otimes A_{uw}+\bar{L}_{ \text{G}}\otimes B_{uw}F_{uw} \tag{32}\]  
Equation (32) right-hand side in the form of \(A_{uw}+\lambda B_{uw}F_{uw}\) where \(\lambda\): eigenvalues of \(L_{\text{C}}\). With the assumption of a second order system, the feedback matrix is in the form of \(F=I_{N}\otimes(f_{1},f_{2})\). Where, we can choose \(f_{1}\) to solve \(f_{2}\) form \(\frac{(g_{22}+\lambda f_{2})^{2}}{4\lambda}<-f_{1}\) for nonzero eigenvalues. A detailed of vehicle formation based graph theory is available in [35].
# Low-Cost Solution for Kinematic Mapping Using Spherical Camera and GNSS
## 2 Materials and Methods
### Case Study--Fleming Square, Prague  
The Fleming Square (50.1050564 N, 14.3918744 E) test site (Figure 2) was chosen for two reasons. This area is close to the CTU campus in Prague, and there are fully grown trees in the square. Due to these reasons, there is jamming of the GNSS signal, and the measurements are more relevant to the real measurement conditions.  
At this site, the necessary GCPs were measured using the GNSS RTK method. Together four GCPs were measured and used in the following processing. The GCPs were measured three times, and the 3D standard deviation was calculated from their differences. The 3D standard deviation of the GCPs was 20 mm, and deviations of the GNSS RTK measurements are shown in Table 1. The square was also surveyed using the Faro Orbis mobile scanning method and the SLAM method.
## 4 Optimal Exciting Trajectory  
In the dynamic parameter identification, the exciting trajectory should be optimized to persistently excite the dynamics of the mechanism. For convenience, either a polynomial or sinusoidal exciting trajectory is typically used. Since the latter is periodic and can in general generate more complicated trajectories, it was used in this work. This trajectory is parameterized by using the following Fourier series:  
\[q_{i}(t)=q_{i0}+\sum\
olimits_{j=1}^{n_{H}}\left(\frac{a_{ij}}{2\pi fj}\sin{( 2\pi fjt)}-\frac{b_{ij}}{2\pi fj}\cos{(2\pi fjt)}\right), \tag{35}\]  
where \(n_{H}\) and \(f\) are the harmonic number and the fundamental frequency in Hz, respectively, whereas the subscript \(i\) corresponds to the \(i\)th generalized coordinate \(q\). The parameters \(n_{H}\) and \(f\) should be predefined. For the mechanism at hand, \(i=1\), \(2\), \(3\), where \(q_{1}\), \(q_{2}\), and \(q_{3}\) are given by the coordinates of the end-effector, i.e., \(x\), \(y\), and \(\theta\).  
It can be observed in Equation (35) that the parameters that should be determined for an optimized exciting trajectory are \(q_{i0}\), \(a_{ij}\), and \(b_{ij}\). The total number of these parameters is:  
\[s=l(2n_{H}+1). \tag{36}\]  
The optimization of the exciting trajectory is performed to optimize an objective function, which indicates the rank of the system subject to some constraints of the mechanism. The constraints used are typically the reachable workspace; collision avoidance; and the limits of the position, velocity, acceleration, and forces of the active joints, zero initial and final velocity, and zero initial and final acceleration. Since the parameterized exciting trajectory is expressed in terms of the new parameters \(q_{i0}\), \(a_{ij}\), and \(b_{ij}\), the objective function \(G\) should also be expressed in terms of these new parameters. In this work, the exciting trajectory optimization could be stated as the following constrained optimization problem: find \(q_{i},\dot{q}_{i}\), and \(\ddot{q}_{i}\) that optimize the objective function \(G\) subject to:  
\[\begin{array}{c}q_{imin}\leq q_{i}\leq q_{imax}\\ \left|\dot{q}_{i}\right|\leq\dot{q}_{imax}\\ \left|\ddot{q}_{i}\right|\leq\ddot{q}_{imax}\\ \dot{q}_{i}(t=0)=\dot{q}_{i}(t=t_{f})=0\\ \ddot{q}_{i}(t=0)=\ddot{q}_{i}(t=t_{f})=0\\ \left|\tau_{i}\right|\leq\tau_{imax}\end{array} \tag{37}\]  
The objective function \(G\) used in this work was a weighted composite of the condition number and inverse of the minimum singular value of the observation matrix \(W\) in the linear form of the inverse dynamics equations with respect to the dynamic parameters [13], i.e.:  
\[G\Big{(}q_{i},\dot{q}_{i},\ddot{q}_{i}\Big{)}=G\Big{(}q_{i0},a_{ij},b_{ij} \Big{)}=w_{1}cond(W)+w_{2}\frac{1}{\sigma_{\min}}, \tag{38}\]  
where \(w_{1}\) and \(w_{2}\) denote the corresponding weights.