esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The land reclaimed from the seaside may have a long-term subsidence trend, which poses a potential geohazard in the future land use. Xiamen Xiang'an New Airport (XXNA) is built on reclaimed land since 2016. Based on the spaceborne Sentinel-1 data between January 2018 to April 2019 and the time series interferometric synthetic aperture radar (InSAR) technique, this paper analyzed the reclaimed land subsidence evolution at XXNA in this period. InSAR measurements show that XXNA is suffering from severe subsidence, mainly in three regions because of the earth and sand compacting. By analyzing the spatial subsidence characterizations of the main subsiding areas combined with historical land reclamation and future land use planning, we find the potential threat of subsidence to future land use. Correlation between subsidence and the period of reclamation was found, indicating that the consolidation and compression in dredger fill is the main cause of subsidence. By combining subsidence monitoring results with different land use types and adopting the Expectation (Ex) and Entropy (En) methods, we analyzed the key area with potential subsidence geo-hazard. This work shows that with SAR interferometry, it is possible to find the large area ground subsidence in the airport reclaimed area. The areas with potential subsidence geo-hazards are consistent with the deep reclaimed earth, which means high subsidence risk in the future.  
InSAR; reclaimed land; subsidence; Sentinel-1; Xiamen Xiang'an New Airport; entropy 2020, 12, 6991; doi:10.3390/s1u2176991
# A Novel Method for Assessing Regional Tendon Stiffness and Its Significance
## 3 Results
### Effects of q-Angle on Percentage Changes in Tendon Shear Modulus  
Table 2 shows that when the patella was laterally displaced, the q-angle and the percentage changes on tendon shear modulus had were positively correlated on the lateral half (r = 0.47, \(p\) < 0.05) but a negative correlation with the medial half (r = \(-\)0.48, \(p\) < 0.05) in the female subjects. No significant association was detected when the patella was displaced towards the medial side. No significant association were observed between the two variables when the patella was displaced in in the male subjects (all \(p\) > 0.05).
## 3 Monthly and Seasonal Variation Characteristics
### Monthly Variation Characteristics  
At the study site during the daytime, the average hourly change in MLH and SMEs in different months is shown in Figure 4. The main way of energy exchange of the land-atmosphere is turbulent sensible heat in the TD [10; 47]. From the perspective of the time span, between 8:00 and 14:00 BJT, with the increase in temperature and wind speed, the ML rises rapidly (Figure 4a). From 14:00 to 17:00 BJT, the change in amplitude of all SMEs is small (Figure 4b-e), and the MLH is always maintained in a high range. From 17:00 to 20:00 BJT, due to the dust aerosols in the atmosphere increasing the surface energy [48], the atmospheric cooling rate is slow (Figure 4b). However, the decrease in the desert surface temperature is much higher than that of the upper atmosphere, which can promote the formation of the inversion layer, and combined with the sharply decreasing wind speed (Figure 4e), the MLH decreases rapidly.  
Figure 3: The seasonal and annual wind frequency rose diagrams during the daytime. (**a**) Spring. (**b**) Summer. (**c**) Fall. (**d**) Winter. (**e**) Annual.  
The monthly average height of the ML reaches its highest at 17:00 BJT. At this moment, the temperature warms up and the wind is strong (2.74 m/s, Figure 4e) in March, which accelerates the evaporation of water and leads to the largest decrease in air relative humidity. In May, the wind speed is the strongest (2.8 m/s, Figure 4e), and the visibility is the lowest among all months (9.4 km, Figure 4d). The warmest month is July (33.83 \({}^{\circ}\)C, Figure 4b), but the highest MLH is in June (3.68 km, Figure 4a). From June to July, although the changes in temperature, relative humidity, and visibility are not significant, the wind speed decrease at the highest rate of all months, thus affecting the degree of turbulent mixing in the ML. From August to September, the desert underlying surface absorbed much heat in summer and began to release heat in September, causing the temperature to rise slightly (30.40-30.61 \({}^{\circ}\)C, Figure 4b). At the same time, the relative humidity and visibility changed the most among all months (Figure 4c,d), and MLH showed an increasing trend. From September to December, the cooling of the atmosphere and the decrease in solar radiation led to the rapid decrease in temperature (30.61-2.45 \({}^{\circ}\)C, Figure 4b).
## 3 Results
### Investigation of the Influence of Image Brightness on the Results of Strain/Stress Analysis
The obtained levels of individual measurement errors are shown in Figure 14. The mean relative measurement error values evaluated from the set of 60 images (30 and 30 from each measurement) for each load level and their standard deviations are shown in Table 4.  
Figure 12.0 Influence of the image brightness and estimated calibration parameters on the evaluated equivalent von Mises stress: (**a**) \(F=400\) N, (**b**) \(F=800\) N, (**c**) \(F=1200\) N, (**d**) \(F=1600\) N. Note: MGV (mean gray value), CTP (calibration target positions). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**F = 400 N**} & \multicolumn{1}{c}{**F = 800 N**} & \multicolumn{1}{c}{**F = 1200 N**} & \multicolumn{1}{c}{**F = 1600 N**} \\ \hline \(\Delta\sigma_{\mathrm{mean}}^{\mathrm{Mises}}\) (\%) & from 6.8 up to 7.5 & from 3.1 up to 3.6 & from 2.1 up to 2.6 & from 1.6 up to 1.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean relative differences in the evaluated equivalent von Mises stresses obtained by varying the image intensity over a range of mean gray values from 56 to 171. Figure 14: Mean relative measurement errors for the analysis of equivalent von Mises stress obtained around the specimen hole for different load levels: (**a**) \(F\) = 400 N, (**b**) \(F\) = 800 N, (**c**) \(F\) = 1200 N, (**d**) \(F\) = 1600 N. Note: M1—measurement 1, M2—measurement 2. Based on the analysis of the obtained results, it can be concluded that the mean differences in the equivalent von Mises stress values achieved when the intensity of the images is changed from the mean grey value of 56 up to 171 only slightly exceed the levels of the evaluated measurement errors. Thus, within the range indicated, there is no significant influence on the results of the realised stress analyses.
# A Second Harmonic Wave Angle Sensor with a Collimated Beam of Femtosecond Laser
## 4 Conclusions  
The SHG angle sensor with collimated Fs beam has been demonstrated by evaluating the SHG spectral peak upon the angular position of MgO:LiNbO\({}_{3}\) crystal that represents the measurement target. A measurement sensitivity of 0.000294 nm/arc-second and a resolution of 6.80 arc-second was achieved in the most extended observable angle range at all crystal positioned variations on the \(Z\)-axis. It indicates that the proposed angle sensor can overcome the problem of target misalignment along the Fs laser propagation direction. The configuration of the angle sensor is suitable for non-contact inspection as well as not being limited by the type of material target, providing the solution to the problems encountered in the use of conventional angle sensors. For these reasons, the proposed angle sensor is expected to have many potential applications, especially in the inspection process in manufacturing industry.  
Conceptualization, W.G. and Y.S.; methodology, W.G. and H.M.; software, W.D.A. and R.S.; validation, W.G., Y.S. and H.M.; formal analysis, W.D.A., K.L. and R.S.; investigation, W.D.A. and K.L.; resources, W.G., Y.S. and H.M.; data curation, W.G. and H.M.; writing--original draft preparation, W.D.A. and Y.S.; writing--review and editing, W.G. and Y.S.; visualization, W.G. and Y.S.; supervision, W.G.; project administration, W.G.; funding acquisition, W.G., Y.S., H.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Japan Society for the Promotion of Science (JSPS): 20H00211.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# The New CAP Theorem on Blockchain Consensus Systems
## 4 The New CAP Theorem
### Autonomy (A) vs. Consensus (C)  
The tradeoff between (A) and (C) is evident through Equation (6). This imposes a condition of mutual exclusion among them as a requirement for the concurrent optimization of the two properties.  
Figure 4 shows the existing tradeoff between C and A with respect to the number of event witnesses (\(W\)).  
Figure 4: Autonomy (A) vs. Consensus (C) as functions of \(W\). As consensus requirements increase rightward, node autonomy reduces downward and vice versa, illustrating an intrinsic limitation.  
Figure 3: **(a)** Consensus as a function of \(W\) in a dynamic system of 100 Nodes, \(W\) varying from 0.1 to \(\mathrm{N}-1\). Consensus increases with the number of witnesses, illustrating that broader node participation leads to increased agreement in the system. **(b)** Autonomy as a function of \(W\) in a dynamic system of 100 Nodes, \(W\) varying from 0.1 to \(\mathrm{N}-1\). Autonomy sharply decreases with increasing numbers of event witnesses (\(W\)), reflecting reduced independence of the nodes as they spend more resources to validating external events.  
The scatter plot in Figure 4 depicts the outcome of Equations (4) and (5) with respect to \(W\) and demonstrates that Autonomy and Consensus cannot be optimized simultaneously in our system: while consensus increases with \(W\), the resources remaining to serve the local events in the nodes decrease, and along with them, the Autonomy of the node declines as well.  
**Figure 4 clearly demonstrates this mutual exclusion condition and constitutes the first proof of the initial assertion (the new CAP Theorem).**  
Still, this is not the only one. As we demonstrate following in Section 4.3, neither Consensus and entropic Performance (P) can be optimized simultaneously, raising the second mutual exclusion condition.  
The constraint between Autonomy (A) and Consensus achievement (C) is also validated through the empirical analysis of real-world large-scale Blockchain system benchmarking data in Section 5.2. As it is clearly presented there, in the case of Bitcoin, the Autonomy (A) is fully sacrificed in favor of maximum Consensus achievement (\(W\to N\) requirement). In the case of Hyperledger Fabric, the achievement of catholic Consensus is compromised, since only a pre-defined set of nodes out of the whole are invoked in the consensus process, requiring \(W=\frac{N}{2}+1\) in the default case, leading nodes to higher Autonomy (A) and the system to high availability. In the case of Ethereum 2.0 (PoS), an elegant tradeoff between (C) and (A) is committed, keeping \(W\) from 128 verifiers initially, to \(\frac{2N}{3}\) validators in order to reach finality secondarily.
# Vibration Reduction in a Pulse Tube-Cooled High Purity Germanium Detector
## 1 Introduction
In order to attenuate the vibration exported, a linear dual-piston compressor comprised a pair of back-to-back in-line pistons resonantly driven by linear \"moving coil\" motors, arranged to oppositely slide inside tightly matched cylinder liners. A flexible gas transfer line (a thin-walled copper tube of a small diameter) was installed to connect the linear compressor and cold head. The flexible gas transfer line isolated the detector vibration interference. Although producing quite acceptable vibration export for most applications, this vibration export was excessive for the HPGe detector. In order to attenuate cooler vibration further, a compressor vibration isolation kit (CVIK) and heat links [9; 10; 11; 12] were implemented. On account of the advantages of the simplicity and reliability of passive-type isolators, they have generally been employed to achieve the desired isolation performance. To attenuate the micro-vibration of the compressor, a compressor vibration isolation kit (CVIK) with eight sets of spring and damper isolators was implemented. Eight sets of damping spring dampers were divided into two groups and distributed evenly around the compressor. The right section of Figure 1 shows a structural diagram. The compressor with the CVIK had the same isolation performance in any direction. Figure 1: Portable EMC vibration reduction system for HPGe detector. This paper evaluates the effectiveness of the CVIK and heat links. A micro-vibration test and HPGe energy resolution tests were performed. The results indicate that the passive isolation method can meet HPGe detector requirements.
## 5 Conclusions  
In this paper, a rotor winding short-circuit-fault protection method for VSPSGM combining the stator and rotor currents with different frequencies is proposed. The following conclusions are drawn:  
(1) According to the various characteristics of stator and rotor currents before and after the rotor winding short-circuit fault, a protection method that combines the stator and rotor currents with different frequencies is proposed. This method does not need to take special treatment measures for rotor speed.  
(2) The protection criterion of the proposed protection is constructed based on the discrete integral operation. Considering the influence of unbalanced voltage and sensor measurement error, the calculation method of the protection setting value is proposed to ensure the reliability of the protection method.  
(3) Simulation results show that the proposed method is effective for faults with different slip ratios and different fault types. It can be combined with the stator branch circulation method to form dual protection for the internal rotor winding short-circuit fault. The protection actionable number is increased by 79 and 745, respectively, compared with the stator branch circulation method and the proposed method. The total protection coverage rate can reach 99.17%. Therefore, when the two methods are configured at the same time, they can achieve complementary advantages and improve the speed and reliability of the overall protection.  
(4) The proposed method can be extended to the motor with three-phase AC excitation, and cannot be applied to the motor with single-phase DC excitation. The next work plan is to further study the protection method of dead zone faults and verify the proposed method by using the fault recorder data that may occur on the actual engineering VSPSGM in the future.  
Conceptualization, J.Q.; methodology, Y.W.; software, R.H.; validation, Y.W. and J.Q.; formal analysis, H.Z.; investigation, W.W.; resources, R.H.; data curation, J.Q.; writing--original draft preparation, Y.P.; writing--review and editing, X.Y.; visualization, Y.W.; supervision, H.Z.; project administration, W.W.; funding acquisition, R.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Key technology project of China Southern Power Grid Co., LTD, grant number STKJXM20210102.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Faults in the Same Branch** & \multicolumn{2}{c}{**Faults between Two**} & \\  & **with Different** & **Branches in the** & **Faults between Phases** & **Total** \\  & **Short Turns** & **Same Phase** & & \\ \hline In-slot faults & 226 & 36 & 0 & 262 \\ End faults & 1554 & 1906 & 8232 & 11,692 \\ Total & 1780 & 1942 & 8232 & 11,954 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The actionable protection number statistical results when both methods are configured.
# Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks
## 2 System Model
### Performance Metric  
In this section, we describe the performance metric to evaluate the performance of the proposed scheme compared to previous works. The first component of the performance metric is overhead performance, which consists of downlink and uplink aspects. There are two kinds of uplink reporting messages. The first one is the \"periodic channel state information reference signal (CSI-RS) report\" reporting the reference-signal-received-power (RSRP) information and beam ID periodically [18]. The second one is the MR, reporting RSRP and beam or cell ID which satisfies the measurement event condition. It is used in the proposed scheme, and in [25]. Therefore, the uplink overhead can be calculated from the message size of \"periodic CSI-RS report\" in the physical-uplink-control-channel (PUCCH) and MR in the physical-uplink-shared-channel (PUSCH). The size of the single periodic CSI-RS report message, \(M_{\rm CSI-RS}\), can be calculated as [40]  
\[M_{\rm CSI-RS}=\lceil\log_{2}K_{\rm s}^{\rm SSB}\rceil+\lceil\log_{2}K_{\rm s }^{\rm CSI-RS}\rceil+M_{\rm RSRP}, \tag{16}\]  
where \(K_{\rm s}^{\rm SSB}\), \(K_{\rm s}^{\rm CSI-RS}\) are the number of the SSB and CSI-RS beams, respectively. \(M_{\rm RSRP}\) is the bits for representing RSRP, which usually comprises 7-bits. Furthermore, the size of a single MR containing the information of the SSB measurement result \(M_{\rm MR}\) can be calculated as [16, 25]  
\[M_{\rm MR}=M_{\rm default}+N_{\rm SSB}\left[M_{\rm RSRP}+\lceil\log_{2}K_{\rm s }^{\rm SSB}\rceil\right], \tag{17}\]  
where \(M_{\rm default}\) is a default size of the MR message and \(N_{\rm SSB}\) is the number of reported beams. In addition, each single message size of downlink beam-switching and handover command are defined as \(M_{\text{BS}}\) and \(M_{\text{HO}}\), respectively. Next, RLF rates, beam switching failure (BSF) rates, and the handover failure (HOF) rates can be calculated from the number of RLF, too-early handover, too-late handover, too-late beam switching, and early beam-switching versus the number of attempted handover (HO) and beam switching (BS). The other performance metric is a 3-dB loss probability representing the accuracy of beam-pair selection. The 3-dB loss probability is represented as [41]  
\[P_{\text{3 dB}}=\mathbb{P}\left[\zeta>2\right], \tag{18}\]  
where \(\zeta\) is the definition of the power loss, i.e., the ratio of the RSRP value between the selected beam pair \(s\) and best beam pair among the \(m\)-possible beam pair set \(\mathcal{B}\). It can be represented as  
\[\zeta=\frac{\max_{m\in\mathcal{B}}\text{RSRP}_{m}}{\text{RSRP}_{s}}. \tag{19}\]  
Table 1 lists the description of symbols used in this section.
## 2 Materials and Methods
### Fragmentation and Window Length  
For classifier training, acceleration signals were fragmented into shorter consecutive fragments. Before fragmentation, the short pauses in the signals between different conducted activities were removed and only signals recorded during activities listed in Table 2 were kept. While some studies opt for an overlap between windows to increase the classification performance, in this study, no overlap was used to keep the computational power minimal.  
In a system with a physical activity classifier working in real time, the window length determines the delay of the system, since each classification is done after signals have been collected for a whole window. The number of samples in the fragment is determined by both the sampling frequency and the window length according to Equation (1).  
To evaluate how different window lengths affect the classifier performance, the window lengths of 5 s, 3 s, and 1 s were chosen, which are near the values usually used for physical activity classification in previous studies [13; 17].
# Autonomous Intersection Management by Using Reinforcement Learning
## 3 Methodology
### Reinforcement Learning for Traffic Control
#### 3.1.2 Action Definition  
The action space \(A=\{a_{1},\ a_{2},\ a_{3},\...,\ a_{|A|}\}\) consists of actions that can be selected by an agent. We will introduce two main definitions here for action space.  
**Traffic phase selection** is the selection of a combination of non-conflicting phases to be triggered at the same time. Each phase at an intersection has a set of timings. As shown in Figure 1, there are eight numbers representing different phases among lanes, where phases of even numbers represent vehicles that can go either straight or turn right, and phases of the odd number represent vehicles that should turn left. The trajectory of movements of phase 1 and phase 5 do not cause a collision; thus, phases (1,5) are called a combination of non-conflicting phases. The number of non-conflicting phase combinations is eight and is presented as follows: {(1,5), (1,6), (2,5), (2,6), (3,7), (3,8), (4,7), (4,8)}.  
**Traffic phase split**: An action consists of combinations of the green timing of each phase, where the green timing is the time interval that allows vehicles to cross an intersection. A cycle consists of a predetermined sequence of traffic phases, and the cycle length is the cycle's time interval. The cycle length is flexible and based on the demand of traffic.
## 3 Graph Theory and Sensor Networks
### Vertex Coloring  
Studying the chromatic number and vertex coloring of a Delaunay graph associated with a sensor network can be very interesting in order to distribute resources efficiently, avoid sensor interference, assign tasks or roles, identify conflicts, and improve route planning.  
By assigning colors to the vertices of the graph, it is possible to find an optimal coloring which minimizes the total number of colors used. This can be applied to distribute resources efficiently, such as transmission frequencies or communication channels. It can also be applied to reduce energy consumption by activating or deactivating several sets of sensors.  
Regarding interference management, vertex coloring helps to avoid interference between adjacent nodes that share communication channels.  
In reference to task scheduling, this graph tool can be useful to assign specific tasks or roles to sensors based on their colors. This provides the implementation of scheduling and coordination strategies, allowing an efficient distribution of responsibilities within the sensor network.  
Finally, vertex coloring can be very important in order to identify conflicts and avoid fault detection. This can be accomplished by planning new routes following some vertices that have the same color.  
As an example, the chromatic number of the Delaunay graph from Figure 3 is 3. Moreover, a minimal vertex coloring for this graph is given as follows: the first color is for sensors \(\{v_{1},v_{7},v_{4}\}\), the second is for \(\{v_{3},v_{6}\}\), and the third one is for \(\{v_{2},v_{5}\}\).
# Bounding Volume Hierarchy-Assisted Fast SAR Image Simulation Based on Spatial Segmentation
## 2 BVH-Assisted Computation of the Target Multiple Backscatter Field
### BVH-Assisted Simulation of Electromagnetic Wave Transmission
Finally, the task of EM wave transmission simulation can be accomplished by transmitting EM waves only to the latticed radiation area at each sampling moment along the azimuth direction.