esa-sceva/satcom-chunk-collection huggingface dataset:
Revealing the Land Subsidence Deceleration in Beijing (China) by Gaofen-3 Time Series Interferometry  
[PERSON]  
1 College of Earth Science, Chengdu University of Technology, Chengdu 610059, China; [EMAIL_ADDRESS] (Y.H.); [EMAIL_ADDRESS] (K.D.); [EMAIL_ADDRESS] (X.S.); [EMAIL_ADDRESS] (C.L.); [EMAIL_ADDRESS] (N.W.); [EMAIL_ADDRESS] (X.Z.)  
[PERSON]  
3 Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of China, Beijing 100048, China 3  
[PERSON]  
1 College of Earth Science, Chengdu University of Technology, Chengdu 610059, China; [EMAIL_ADDRESS] (Y.H.); [EMAIL_ADDRESS] (K.D.); [EMAIL_ADDRESS] (X.S.); [EMAIL_ADDRESS] (C.L.); [EMAIL_ADDRESS] (N.W.); [EMAIL_ADDRESS] (X.Z.)  
[PERSON]  
5 Huffington Department of Earth Sciences, Southern Methodist University, Dallas, TX 75275, USA; [EMAIL_ADDRESS]  
[PERSON]  
6 National Satellite Ocean Application Service, Beijing 100081, China; [EMAIL_ADDRESS]  
[PERSON]  
The spaceborne time series InSAR technique is an effective method to detect and quantify small displacements with wide coverage and high accuracy [4; 5], which has been widely used in monitoring land subsidence [6; 7; 8], landslides [9; 10], volcanoes [11], mines [12], and glaciers movements [13]. Several researchers have focused on the spatial-temporal dynamics of land subsidence over Beijing via InSAR based on varying time series methods and SAR datasets and have analyzed the causes of land subsidence by natural factors or anthropogenic activities (Table 1). However, few studies have been conducted for land subsidence based on Gaofen-3 SAR images using time series InSAR technology.  
The Gaofen-3 satellite was launched on 9 August 2016. It is the first fully C-band polarimetric SAR satellite in China and is capable of acquiring high-resolution and large-width SAR images. It has been widely used in the application of ocean wind, ship detection, and terrain survey [14; 15; 16; 17]. [PERSON] et al., 2019 made an attempt to use only five Gaofen-3 SAR images to perform the land subsidence over Beijing using InSAR technology [18]--except that there is limited research and applications related to land subsidence monitoring based on Gaofen-3 SAR images via time series InSAR technology due to its imperfect interferometric performance. Therefore, it is essential to evaluate the interferometric performance evaluation of Gaofen-3 SAR images over Beijing.  
In this study, we evaluate the interferometric performance of Gaofen-3 SAR images to demonstrate its capability of interferometric and measurements, refine the quality of Gaofen-3 interferograms, and map the mitigation of land subsidence in Beijing in recent years based on Gaofen-3 SAR images. We apply the total coherence model to identify and discuss the major factors that impact the phase coherence and quality of Gaofen-3 interferograms and refine the artifacts in interferograms induced by inaccurate orbit information. We use time series InSAR (TS-InSAR) technology to generate high-accuracy subsidence maps based on the refinement interferograms of Gaofen-3 SAR images to reveal the mitigation of Beijing land subsidence in recent years.
## 2 Measuring Principle  
The measuring principle of the LCI is based on the interferometric evaluation of the change in optical path length. One big advantage of the LCI method is the independence between axial resolution \(\sigma_{z}\) and lateral resolution \(\sigma_{(x,y)}\). While the axial resolution and the axial measuring range depends on the used light source, shown in Equations (1) and (2), the used scanning lens and scanning system have a big impact on the lateral resolution, shown in Equation (3) [22].  
Assuming a Gaussian beam axial resolution and range and lateral resolution are defined by:  
\[\sigma_{z}=\frac{2\ln 2}{\pi}\frac{\lambda_{0}^{2}}{\Delta\lambda}, \tag{1}\]  
\[\Delta_{z}=\frac{N}{4n}\frac{\lambda_{0}^{2}}{\Delta\lambda}, \tag{2}\]  
\[\sigma_{x,y}=\frac{\Delta\lambda}{\pi}\frac{f}{d}, \tag{3}\]  
where \(\Delta\lambda\) is the spectral width and \(\lambda_{0}\) the central wavelength of the light source, n the average refractive index of the sample, N is the number of used pixels of the detector, d is the beam diameter at objective lens entry and f is the focal length of the used objective lens [29; 31]. A nomenclature of the dimensions and quantities used can be found in the Appendix A.
## 5 Results
### Performance Evaluation of DVB-T2 Using BER  
#### 5.1.1 Native DVB-T2 Without LDPC Coder  
In this part, the CFO impact on the native DVB-T2 system is shown when a fading channel is used or not.  
Figure 12 shows the BER evolution as a function of the SNR in the native DVB-T2 when only Gaussian noise was used for various CFO values. One can note that when the CFO was not applied (CFO = 0.00), the SNR value was equal to 18.4 dB at a BER of \(10^{-4}\). At a SNR value of 20 dB, the BER values of \(5.4\times 10^{-6}\), \(1.2\times 10^{-5}\), \(10^{-4}\), \(8.3\times 10^{-4}\), \(4.2\times 10^{-3}\), and \(1.4\times 10^{-2}\) were obtained for CFO values of 0, 0.01, 0.02, 0.03, 0.04, and 0.05, respectively. These results show that the system performance decreased in the presence of an increasing CFO and the penalty obtained increased with the CFO values. At a BER of \(10^{-2}\), the loss gathered varied from 0.2 dB to 7.8 dB. The main reason for the signal performance degradation was the ICI induced by the CFO on the noisy signal. This ICI was noticed by the offset in the OFDM subcarriers. This offset was materialized by the degradation of the symbols carried by these subcarriers.  
Figure 13 presents the BER evolution as a function of the SNR in the native DVB-T2 when a TU6 channel and Gaussian noise were used for various CFO values. It is remarkable that when the CFO was not applied, the SNR value equaled 42 dB at a BER of \(10^{-4}\). This value (42 dB instead of 18.4 dB) shows the TU6 channel's impact compared to the simulation results of Figure 12. In other words, the TU6 channel caused a loss of 23.6 dB on the system performance. Also, at the SNR value of 40 dB, the BER values of \(1.5\times 10^{-4}\), \(1.6\times 10^{-4}\), \(2.1\times 10^{-4}\), \(3.3\times 10^{-4}\), \(4.8\times 10^{-4}\), and \(2.6\times 10^{-3}\) were obtained for CFO values of 0, 0.01, 0.02, 0.03, 0.04, and 0.05, respectively. One can note the performance degradation with the CFO values increase when the TU6 channel was used. At a BER of \(10^{-2}\), the loss induced by the CFO varied up to 6 dB, depending on the CFO value. The increase in BER degradation was due to the \"multipath\" nature of the channel, which added a time dispersion between symbols (ISI) to the frequency dispersion due to the CFO (ICI). The trends observed in our simulation results are in line with the theoretical models developed in [44].  
Figure 12: BER vs. SNR in the native DVB-T2 system without coding: CFO impact evaluation in the AWGN case.
## 5 Application to the Satellite Module Layout Problem
### Configurations of Both Stages  
#### 5.1.1 Upper Stage Settings  
The operators of the GA are set using a parametric study, and the hyperparameters are optimized using Bayesian Optimization [134]. Among the obtained settings, the population size is set to 250 individuals and the number of generations is set to 700.
## 4 Data Processing  
### Ndvi  
Two types of calibrations were done for the narrow-band sensors by the manufacturer before delivery; absolute calibration for the hemispherical sensors and those with 60\({}^{\circ}\) FOV, and relative  
\begin{table}
\begin{tabular}{l l l l} \hline
**Target** &
\begin{tabular}{l} **Reflectance (or** \\ **transmittance)** \\ \end{tabular} & **Fractions of absorption (or interception)** & **Reference** \\ \hline Canopy1  & \(\tau PAR_{m}=\frac{TPAR}{PAR_{0}}\) & \(fIPAR_{m}=\frac{PAR_{0}-TPAR}{PAR_{0}}=1-\tau PAR_{m}\) & [45] \\ Canopy & \(\tau PAR_{c}=\frac{RPAR_{c}}{PAR_{0}}\) & \(fAPAR_{c}=\frac{PAR_{0}+RPAR_{g}-TPAR-RPAR_{eco}}{PAR_{0}}\) & [45] \\  & & \(=1-rPAR_{eco}-(1-rPAR_{g})\cdot\tau PAR_{m}\) & \\ Ground & \(rPAR_{g}=\frac{RPAR_{g}}{TPAR}\) & \(fAPAR_{g}=\frac{TPAR-RPAR_{g}}{PAR_{0}}\) & [46] \\  & & \(=(1-rPAR_{g})\cdot\tau PAR_{m}\) & \\ Eco- & \(rPAR_{eco}\) & \(fAPAR_{e}=\frac{PAR_{0}-RPAR_{eco}}{PAR_{0}}\) & \\ system & \(=\frac{RPAR_{eco}}{PAR_{0}}\) & \(=fAPAR_{c}+fAPAR_{g}\) & [47] \\  & & \(=1-rPAR_{eco}\) & \\ \hline \end{tabular}
\end{table}
Table 6: Formulae for fractions of PAR absorption and interception, as well as for PAR reflectance and transmittance. \(\tau PAR_{m}\) is the measured canopy transmittance, \(RPAR_{c}\) is the purely canopy-reflected PAR which is a part of the total reflected PAR, \(RPAR_{eco}\), after subtracting fluxes that directly reach the sensor from ground reflection, or after canopy transmission. \(RPAR_{g}\) is the ground reflected PAR, including reflection from bare soil and ground vegetation below the tree canopy. See Section 2.2 for explanation of measured PAR quantities.  
calibration for the sensors with 25\({}^{\circ}\) FOV. For sensors with absolute calibration, narrow band reflectance was calculated by dividing the reflected radiance \(L\) [\(\mathrm{\SIUnitSymbolMicro mol\cdot s^{-1}\cdot m^{-2}\cdot sr^{-1}}\)] with the irradiance \(E\) [\(\mathrm{\SIUnitSymbolMicro mol\cdot s^{-1}\cdot m^{-2}}\)], and then multiplied by \(\pi\) [sr]:  
\[\rho=\frac{\pi L}{E}. \tag{7}\]  
After the reflectance was computed, NDVI was computed using Equation (3). For the sensors without absolute calibration, narrow band reflectance could not be calculated. While ratio-based vegetation indices can still be obtained from the relative sensitivity, e.g., NDVI and simple ratio, non-ratio-based vegetation index, e.g., EVI2 [48] cannot be calculated.  
The data loggers recorded flux signals and internal temperature with 10-minute intervals resulting in 144 diurnal records for each channel. Daily solar noon NDVI values were calculated from these records. Temperature varies from around \(-20\)\({}^{\circ}\)C to \(25\)\({}^{\circ}\)C at our test sites, and the temperature drift of the light sensors was compensated by analyzing the mid-night measurements in relation with the simultaneous temperature. This compensation is especially critical when the daytime incoming light is weak. For the Abisko forest, the incoming light at winter noon is 0.4% of summer peak values in our data, which leads to 15 times lower S/N ratio in winter than in summer based on shot noise theory [49].
# Position-Monitoring-Based Hybrid Routing Protocol for 3D UAV-Based Networks
## 3 Methods
### PMHRP Working Principle  
The data forwarding mechanism aims to establish communication among the UAVs. After selecting the PHC-UAV node in a UAV-based network, the PMHRP routing protocol exchanges the number of routing packets to introduce PHC with all active UAVs. The PHIMP uses GPS positions and establishes the shortest path. Figure 5 shows the working environment of PHIMP with adjacency matrix, root update, and root response messages. The selected PHC node maintains the routing information in 6 steps for the whole network: (1) The PHC-UAV broadcasts a root update message which contains an empty adjacency matrix to normal UAVs. (2) On receiving the root update message, each UAV updates the adjacency matrix with its GPS location coordinates accordingly and forwards it to neighboring UAVs. (3) UAVs send root response messages to the PHC-UAV with a filled adjacency matrix. (4) After receiving the filled adjacency matrix of all UAVs, the PHC stores that information. This is the method through which PHC stores the locations of all UAVs. (5) PHC fills binary information into a new adjacency matrix with the help of locations of all UAVs and broadcasts it to all UAVs. (6) Every UAV performs the Dijkstra algorithm on the binary adjacency matrix to find the shortest path between sources to destination. In this way, all UAVs have routing information for the entire network, which overcomes the problem of position-based routing protocol because position-based routing protocols have only local routing information instead of the whole network.  
Figure 4: Flowchart of PMHRP: (**a**) PHC Selection; (**b**) Data Forwarding.
# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 2 Image Watermarking Background
### Embedding Phase  
In this phase, the original image \(I_{0}\) is combined with the message \(m\), which is a collection of bits encoding the data to be added. This operation's goal is to create a watermarked image \(I_{w}\). The watermarked images are perceptually identical to the original images. The message \(m\) is encoded using a secret key \(K\) to generate a mark \(W\). To maintain the imperceptibility requirement between \(I_{w}\) and \(I_{0}\)[31], the mark is then modulated and/or scaled. There are two ways to insert a mark \(W\) into an image \(I_{0}\)--additive or substitutional.
###### Abstract  
Determining the displacements and consequent deformations of structures is a demanding branch of engineering. Displacements are most often determined by geodetic methods, among which high-precision non-contact methods have recently taken the lead. Engineering geodesy is an indispensable part of construction projects. In the desire for efficient and fast measurements, the technology of terrestrial laser scanning (TLS) and the use of robotic total station (RTS) and other geodetic methods are becoming more and more useful for engineers. In the presented study, we focused on the measurement and comparison of vertical displacements with various mentioned equipment and the determination of the influence of meteorological conditions on the displacements of timber beams that we used to perform the experiment. Measurements were performed both in the laboratory and outdoors. A novelty in the work was the use of a TLS scanner to determine the evaluation of small value displacements and the analysis of the usability of geodetic measuring equipment. In the Materials and Methods section, we describe the equipment used and the characteristics of the beams. The Results section describes the experimental outcomes, which include the performance of experimental analysis of vertical displacements of timber beams under different meteorological conditions. Altogether, the results consist of geodetic measurements and the processing of measured data. The results of measurements of vertical displacements with a terrestrial laser scanner were compared with the results obtained with a robotic total station were evaluated and compared with the displacements calculated from static analysis and the results of other methods used.  
terrestrial laser scanning; displacements; geodesy; timber beams; robotic total station +
Footnote †: journal: Applied Sciences
# SOLIOT--Decentralized Data Control and Interactions for IoT
## 8 Evaluation
That is noteworthy regarding the fact that the handling of IoT requests takes longer than their HTTP counterparts (right side of columns in Tables 8 and 9, rows '@Server'). The implication is that the reduced data size results in a higher network speed. Furthermore, the SOLIOT prototype heavily relies on the core SOLID code. It mainly wraps the IoT endpoints but calls the underlying LDP functions in the same way as the SOLID baseline server does. Having a native integration directly on the persisting functions should further increase its speed. Figure 9 further supports this insight. One can easily see the speed advantage. The interested reader may note the marked outlier for one CoAP get request. This one might be caused through the still at some times unreliable behavior of the SOLIOT prototype. Another relevant insight is the difference in the behavior of GET requests for both SOLID (faster than the others) and SOLIOT (slower), an effect which requires further investigations. All collected measures together with the resources to reproduce the measurements are provided publicly [55]. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Resource** & **SOLIOT** & **Size** & **Resp.** & **SOLID** & **Size** & **Resp.** \\  & **Interaction** & **(bytes)** & **Size** & **Interaction** & **(bytes)** & **Size** \\ \hline /th[...]/maximumTemperature * & CoAP get & 398 & 641 & HTTP GET & 204 & 1495 \\ /things/[...]/currentfemperature & CoAP get & 398 & 705 & HTTP GET & 204 & 1556 \\ /things/357 PM0 CP4 BD/120636/ & CoAP get & 308 & 7067 & HTTP GET & 161 & 8853 \\ /things/ & CoAP get & 109 & 2426 & HTTP GET & 142 & 3324 \\ /things/[...]/testTemperature & CoAP put & 980 & 0 & HTTP PUT & 810 & 704 \\ /things/[...]/testTemperature & pub: updated & 675 & – & HTTP PUT & 810 & 704 \\ /things/[...]/testTemperature & CoAP get & 406 & 372 & HTTP GET & 203 & 1218 \\ /things/[...]/testTemperature & CoAP delete & 406 & 128 & HTTP DELETE & 190 & 627 \\ /things/[...]/ & CoAP post & 980 & 0 & HTTP POST & 828 & 772 \\ /things/[...]/ & pub: created & 675 & – & HTTP POST & 828 & 772 \\ /things/[...]/testTemperature & CoAP get & 406 & 960 & HTTP GET & 200 & 1212 \\ /things/[...]/testTemperature & CoAP delete & 406 & 128 & HTTP DELETE & 187 & 666 \\ \hline \hline \end{tabular}
* complete URL for the first entry: coap://18.157.197.66:5683/things/35/PM0 CP4 BD/120636/2018-10-24T01-22-30-866Z/maximumTemperature.  
\end{table} 
Table 10: Interaction sequence for the evaluation setting. Each iteration was executed 10 times (in total 100 CoAP and 20 MQTT interactions) per test instance. Presented are average measures per request.
# Revisiting the Design of Parallel Stream Joins on Trusted Execution Environments
## 3 Challenges of Parallelizing Stream Joins on TEEs
### Execution Approaches  
We consider two fundamental _execution approaches_ of parallel stream join: _lazy_ and _eager_. The _lazy_ approach initially buffers all input tuples of from both input streams over a given time window, and then joins a complete set of tuples. In contrast, the _eager_ approach actively joins subsets of input tuples upon their arrival. Due to context and application dependency, it is difficult for researchers and practitioners to converge on an optimal approach to adopt. For instance, a lazy approach may be beneficial as it reduces enclave function call overheads and it can apply specific data-dependent optimizations before processing joins. However, it may also introduce additional processing latency due to the non-trivial performance overhead of EPC page swaps. In contrast, the length of a subset being processed under the eager approach is tunable. By tuning \(N_{R}\) and \(N_{S}\), we may be able to achieve better processing performance through balancing the trade-offs between function call and memory allocation overheads.
## 4 Mathematical Model and Distinct Features of Active Dynamics
### Mathematical Model of Active Dynamics in Two Different States  
With an oscillating coil as the actuator, the linear relationship between the active ampere force \(f_{\rm a}(t)\) and the excitation current \(i(t)\) can be expressed as Equation (15).  
\[f_{\rm a}(t)=Bli(t)=k_{\rm M}i(t) \tag{15}\]  
where \(B\) is the magnetic induction intensity, \(l\) is the coil length, and \(k_{\rm M}\) is the voice-coil constant.  
The model for the active dynamics analysis is based on two different states: a fluid-filled state and a non-fluid state. The input of the dynamics in both states is the actuator current \(i\). For the fluid-filled state, the outputs are the reaction force \(f_{1}\) and transmitted force \(f_{5}\). However, for the non-fluid state, the outputs are the mover acceleration \(\ddot{y}_{3}\) and transmitted force \(f_{5}\) to the chassis side.  
In the fluid-filled state, because the engine and chassis sides are fixed, and the boundary conditions can be expressed as \(y_{1}=0\) and \(y_{5}=0\) based on the mechanical model shown in Figure 2. The nonlinear mathematical model of the active dynamics can then be obtained using Equation (16).  
\[\left\{\begin{array}{l}\rho l_{2}\ddot{y}_{2}+\frac{1}{2}\rho\big{(}\dot{x} _{12}+\dot{x}_{23}\big{)}\big{|}\dot{y}_{2}\big{|}\dot{y}_{2}=-p_{1}\\ m_{3}\ddot{y}_{3}+c_{3}\dot{y}_{3}+k_{3}y_{3}+A_{3}p_{1}=f_{\rm a}\\ A_{2}y_{2}+A_{3}y_{3}=p_{1}/K_{1}\\ f_{\rm a}(t)=Bli(t)=k_{\rm M}i(t)\\ f_{1}=-A_{1}p_{1}\\ f_{5}=c_{3}\dot{y}_{3}+k_{3}y_{3}-(A_{1}-A_{3})p_{1}-f_{\rm a}=f_{1}-m_{3}\ddot{ y}_{3}\\ \end{array}\right. \tag{16}\]  
\[\left\{\begin{array}{l}m_{3,\rm nof}\ddot{y}_{3}+c_{3,\rm nof}\dot{y}_{3}+k_ {3}y_{3}=f_{\rm a}\\ f_{\rm a}(t)=Bli(t)=k_{\rm M}i(t)\\ f_{1}=0\\ f_{5}=c_{3,\rm nof}\dot{y}_{3}+k_{3}y_{3}-f_{\rm a}=-m_{3,\rm nof}\ddot{y}_{3} \\ \end{array}\right. \tag{17}\]  
It should be noted that in the non-fluid state, the AHM-IT-DM-OCA is incomplete and significantly different. First, the mount is not filled with fluid, and the main rubber spring is not compressed. The chassis side is fixed, and there is no force transmitted to the engine side when the actuator is operating; therefore, \(f_{1}=0\). Second, in the non-fluid state, the mover mass and damping are different from those in the fluid-filled state; set model \(m_{3}=m_{3,\rm nof}\) and \(c_{3}=c_{3,\rm nof}\). Third, the linear stiffness of the decoupler membrane remains \(k_{3}\), regardless of whether it is in a non-fluid or fluid-filled state.  
Based on the mechanical model shown in Figure 2, setting \(y_{1}=0\) and \(y_{5}=0\), a linear model for the active dynamics of the AHM-IT-DM-OCA in the non-fluid state can be obtained as Equation (17).
# An Efficient Task Autonomous Planning Method for Small Satellites
## 2 Question Description and Modeling  
### Problem Description  
The working environment on the satellite changes dynamically over time, which is mainly reflected in two aspects, the tasks and satellite status. The change of tasks is determined by the tenant's needs and weather conditions, for example, the user may propose a new task at any time, and some tasks may be cancelled temporarily because of cloud cover. The change of satellite state means that every task performed by the satellites will have some influence on the on-board memory, battery, and other resources, and the impact cannot be accurately predicted before the task is completed.  
Therefore, the autonomous task planning problem of small satellites can be described as follows: A set of tasks need to be accomplished by small satellites, but the small satellites are limited in their computing capabilities and on-board resources. The task planning can efficiently accomplish the observation task and the data-downloading tasks without intervention of the ground. In this way,the satellite can not only respond quickly to task demands, but can also perform as many tasks as possible, providing maximum observation profits.  
The difficulties are as follows:  
1. Generally, there are numerous constraints in satellite task planning. The task planning of the satellites [24; 25; 26; 27] is Non-deterministic Polynomial hard [28], taking the time-dependency and resource-dependency into consideration, hence, making the modeling even more difficult.
2. It is difficult to properly design the mode and framework so that real-time response and optimization of task planning can be integrated on board.
3. The operation ability of small satellite is limited, and the planning algorithms need to be adapted to the resources on the satellite. For this reason, it is difficult to design an efficient algorithm.  
In this article, we analyzed the previous research and proposed a model based on the following assumptions.  
**Assumption 1**.: _A single satellite has only one remote sensor._  
**Assumption 2**.: _The satellite cannot be charged during the time of umbra or penumbra, while it can be charged constantly during the rest time._  
**Assumption 3**.: _We divided tasks into common tasks and data-downloading tasks. The data-downloading tasks do not participate in the optimization._  
**Assumption 4**.: _The data-downloading tasks automatically execute when the following conditions are met: (1) the satellite runs in the time window; (2) there is data in the on-board memory; and (3) electricity is available (as shown in Figure 1)._  
**Assumption 5**.: _The planning system can communicate effectively with other systems on the satellite and execute the corresponding commands in a timely manner._