esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
[27] derived the Bohai SSCs from the GOCI data using the MCC method, and evaluated the results using the Oregon State University (OSU) tidal model and high frequency (HF) radar observations, showing that for a time difference of 1 h, a template window size of 10-15 km, and a search range of 4-8 km give the optimal results. [PERSON] et al. [28] integrated multivariate optimum interpolation (MOI) with the MCC, and the average absolute differences between the \"actual\" velocity of the derived model and the velocity derived from sea-surface temperature (SST) were reduced by 19% in relative magnitude and by 22% in direction. Previous studies lacked consideration of the method itself, and the MCC method is a template-based matching technique. The reason for this method to derive spurious SSCs is that the nonlinear deformation and movement of water masses and the small-scale dynamic process in the template window may cause the image mismatch in the MCC method in high-turbidity water [29]. Furthermore, the rapid settlement and resuspension of suspended solids in high turbidity water will also affect the precision of SSC field inversion results [10]. SSCs can also be calculated by using ocean models. OSU developed the China Sea Regional Tidal Model TPXO-CSI2016 (China Seas & Indonesia 2016), which covers the South China Sea, East China Sea, Yellow Sea, Bohai Sea, and Northwest Pacific, and assimilates a large amount of satellite remote sensing data and tide gauge data with a spatial resolution of 1/30\({}^{\circ}\)[30; 31]. This model can be used for tidal prediction or tidal current calculation in specific regions. [PERSON] et al. [32] calculated the M2 tidal current using the OSU regional tidal model, and the overall OSU-derived, GOCI-derived, and measured results exhibited better consistency. [PERSON] et al. [33] evaluated the accuracy of seven global/regional tidal models by using the harmonic constants and tidal heights of eight main tidal components of 33 tidal stations in the coastal area of Zhejiang, and considered the model to have high accuracy in the study area. [PERSON] et al. [34] studied the applicability of GOCI inversion SSCs and OSU model SSCs in the Yellow Sea and concluded that the OSU model had high accuracy in simulating the current direction of offshore high-turbidity water. We combine the GOCI data with in situ data to study the accuracy of GOCI-derived SSC field in this paper. To improve the MCC algorithm, we propose a novel multi-correlation coefficient optimization algorithm for GOCI inversion of SSC field vectors based on the tidal ellipse and OSU tidal model. We determined the rotation direction using the tidal vector to identify the spurious vector and substituted it with the appropriate vector to improve the inversion precision. The remainder of this article is arranged as follows. Dataset and methodology used are described in Sections 2 and 3, respectively. Main results and discussion are given in Sections 4 and 5, respectively. Conclusions are provided in Section 6.
## 7 Conclusions  
This paper investigates and conducts experiments on InSAR interferometric imaging and DSM generation under the four-satellite interferometric imaging mode of the PIESAT-01 constellation. It elucidates the principles and technical processes involved in the bistatic SAR interferometric CS imaging algorithm and multi-baseline DSM production. The imaging and DSM production experiments were conducted on the data acquired by the PIESAT-01 constellation over the Qinghai region. The imaging experiment results validated the good focusing performance of the interferometric imaging algorithm, while the coherence of the registered data reflected the high coherence of PIESAT-01 data. Subsequently, the interference processing and DSM reconstruction results confirmed the phase coherence of the interferometric imaging algorithm. Following accuracy validation by ICESat-2 global geolocated photon data and TanDEM data, the elevation accuracy of the DSM exceeded 1 m. Through validation across various terrain regions, the study demonstrated the excellent InSAR capabilities of the PIESAT-01 constellation, explored the effective data support provided by PIESAT-01 data in research on multi-baseline imaging and multi-baseline unwrapping, and offered exemplary data cases for relevant industries. Therefore, as the world's first \"Cartwheel\" formation constellation with four satellites, the PIESAT-01 constellation has successfully achieved imaging and DSM production, providing a foundation for the rapid acquisition of high-precision global DSM and laying a solid groundwork for the overall construction of the \"Nuwa\" constellation in the future.  
Conceptualization, T.Z.; funding acquisition, Y.W.; investigation, J.F. and Y.W.; methodology, J.L., Q.G. and S.G.; supervision, C.L. and Y.Q.; visualization, Q.G.; writing--originaldraft, T.Z.; writing--review and editing, T.Z., [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
Thanks to the Satellite Operations Division and Advanced Algorithm Research Institute of PIESAT Information Technology Co., Ltd. for their platform and technical support for this article.  
The authors declare no conflicts of interest. Author [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] were employed by the company PIESAT Information Technology Co., Ltd., (Beijing, China). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Tele-Operated Bilateral Control for 3 DOF Hydraulic Manipulator  
### Tele-Operated Bilateral Control  
The client and server systems are turned ON. The client, in this case, is the slave, and the server is the master robotic system. The essential safety procedures are followed to ensure that no harm can be done to the environment due to leakage of hydraulic fluids or electric wires. The application of the slave robotic system is carried out after the execution of the master side. Both applications are Windows-based desktop applications developed using Microsoft Visual Studio. The slave robotic system has the parameters required for a server to send a connection request. The master robotic system remains in the listening state, waiting for incoming connections through the network interface on the known IP and port number. When the master robotic system receives an incoming connection request, it verifies the legitimacy of the client. The client completes the verification request by providing the requested parameters; only then does the server establish a communication channel. The application displays the connection status and shows a notification when the connection has been successfully established. The amplifiers are turned OFF, and after a small delay, they are turned back ON by the master and slave applications when connection has been established. The application includes buttons for turning the amplifiers OFF and ON. Operation starts from the master side, and this is subsequently followed by the master upon pressing the Read button on both sides. The master application acquires the data from encoders attached to the axes of the robotic system and computes the desired parameters according to the system equations. These parameters are vital for our tele-operated robotic system. The computed parameters, along with the orientation of axes, are then disseminated to client side. The network sockets are used to transfer information between the master and the slave robotic systems. The slave/client application receives the information from the master through its socket. The slave robotic system is then actuated by the application according to the parameters provided by the master. The slave application also computes the reaction force while moving similarly to the master robotic system. The computation is performed using the SMCSPO algorithm. The slave application measures the position and estimates the perturbation. This information is sent back to the master robotic system. This flow of information continues, and the system has been tested over short and long durations. The slave robotic system keeps tracking the master robotic system. The system flow diagram is shown in Figure 3. If either side (master or slave) stops the application or the network is disconnected, the other side receives a notification of connection closure. The connection-related notifications are shown, along with the application environment, in Figure 4.  
Figure 3: Schematic diagram of tele-operation.
## 3 Data Analysis
### Two-Factor ANOVA on Controller Situational Awareness Related Indicators
#### 3.2.3 Analysis of ATC Information Comprehension Ability  
Understanding and recognition of the outside world pertain to humans' knowledge and evaluation of the essential components inside the perceptual memory process, along with the interconnections among those components [33]. The comprehension part entails integrating distinct perceptual information into a cohesive situational awareness via processes including recognition, analysis, and assessment. This component necessitates the amalgamation of knowledge to comprehend its influence on an individual's objectives and trajectory. The aviation safety report system indicates that pilots' failure to comprehend the situational context accounts for approximately 23.3% of incidents [34]. At this stage, the human brain does cognitive processing, integrating prior experiences and knowledge to understand the environment with the performance evaluation criteria of the control unit. The author developed a working memory assessment scale of 10 self-assessment tasks to evaluate controllers' working memory. This scale mainly assesses controllers' working memory performance in remote tower control operations based on their subjective experiences post-task execution, emphasizing their comprehension of information during the development of situational awareness.  
When the data were checked for normality, all of the groups had \(p\)-values above 0.05, which means the data had a normal distribution, and two-way ANOVA could be used. The two-way ANOVA principally examined the impacts of controller credentials and tiredness levels on several eye movement characteristics. The findings demonstrated that both the tiredness state (F = 125.3, \(p<0.0001\)) and controller qualifications (F = 13.84, \(p=0.0016\)) had substantial major impacts. Moreover, there was no notable relationship between the tiredness state and qualifications about the information comprehension phase (F = 2.264, \(p=0.1497\)). The controllers' weariness significantly affected their comprehension of information. Figure 5 demonstrates that while the tiredness levels of remote tower controllers fluctuated, both seasoned and novice controllers showed a decline in their working memory assessment scores. The deterioration of working memory and information comprehension was associated with the tiredness levels of controllers, with novice controllers exhibiting a more rapid drop than their seasoned counterparts.
## 4 Conclusions  
In this study, a new algorithm for introducing a coarray Fortran (CAF) parallel processing scheme into an existing computer program parallelized only by OpenMP for dynamic frequency-domain soil-structure interaction analysis was proposed and implemented in KIESSI-3D. The speed-enhancement performance of the algorithm according to the number of images (or coarrays) and the number of cores per image was evaluated. As analysis examples for performance evaluation, a full-scale nuclear power plant containment building with both shallow and deep foundation models was used. The results of the numerical-example analyses showed that the new KIESSI-3D improved the analysis rate by an average of 2.78 times for the shallow foundation problem and 2.69 times for the deep foundation  
Figure 10: Parallelization ratio \(P\) achieved by the OpenMP+CAF version using dual-CPU computer systems: (**a**) Shallow foundation example; (**b**) Deep foundation example.  
Figure 9: Speedup ratio \(S\) according to [PERSON]’s law.  
problem compared with the existing KIESSI-3D, thanks to the new algorithm and the CAF incorporation. In addition, as the number of cores and the size of the internal memory of the computer system increased, the parallel processing efficiency increased. Furthermore, when the number of cores was 64, the parallelization ratio of the source code was shown to be 0.977 to 0.988, which is close to the theoretical maximum value of 1. Therefore, it can be confirmed that the new source code is an effective modification for a multi-core computer system.  
Conceptualization, J.-M.K.; methodology, J.-M.K.; software, J.-M.K.; formal analysis, J.-S.L. and H.-J.L.; data curation, [PERSON]-[PERSON] and [PERSON]-[PERSON]; writing-original draft preparation, [PERSON]-S.L.; writing-review and editing, [PERSON]-[PERSON] and [PERSON]-[PERSON]; supervision, [PERSON]-M.K.; funding acquisition, J.-M.K. All authors have read and agreed to the published version of the manuscript.  
This study is supported by Korea Energy Technology Evaluation and Planning (KETEP) funded by the Ministry of Trade, Industry and Energy (No. 20161520101130) and Basic Science Research Program through the National Research Foundation of Korea (NRF) grant funded by the Ministry of Education (NRF-2020R11A3069396).  
Not applicable.  
The data presented in this study are available in article.  
We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work. There is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, \"Improvement of Runtime Speed for Frequency Domain Soil-Structure Interaction Analysis Using a Coarray Parallel Processing Technique\".
## 3 Speckle Noise vs. Incoherent Averaging  
As described above, in scattered GNSS signals from rough surface, there is an importance noise called as speckle noise which occurs as a result of constructive and destructive interference between the signals scattered by different scattering unit in the glistening zone. [PERSON] et al. [29] discussed the statistic of speckle noise from rough surface scattering theory, and found that the probability distribution of the single snapshot took the form of an exponential as well as the discussion in [28] for UK-DMC data. The same distribution could be obtained using UK-TDS-1 data as shown in Figure 2. The significant approach to reduce the impact of this noise on the waveform is to incoherently average a number of consecutive single snapshot as  
\[\langle|Y(\tau)|^{2}\rangle=\frac{1}{N_{\mathrm{incoh}}}\sum_{i=1}^{N_{ \mathrm{incoh}}}|Y_{i}(\tau)|^{2} \tag{7}\]  
where \(N_{\mathrm{incoh}}\) is the number of incoherent averaging. The standard deviation of the peak of the measured waveform could be estimated to quantify the influence of the speckle noise. According to the results of [PERSON] and [PERSON], the standard deviation of the incoherently averaged waveform decreases as a function of the number of incoherent averaging as  
\[\sigma=\frac{\sigma_{0}}{\sqrt{N_{\mathrm{incoh}}}} \tag{8}\]  
where \(\sigma_{0}\) is the standard deviation of the single snapshot. Figure 3 presents the reducing tendency of the standard deviation as the number of incoherent averaging increases, in which the dotted line is the fitted using Equation (8). When the number of the incoherent averaging is larger than 1000 which is usually considered as the incoherent time for GNSS-R, the trend of decreasing becomes slow, however, it is still necessary to adopt the incoherent averaging number over 1000 to further improve the measurement accuracy. As given in [30], regardless of C/A and P(Y) code for altimetry, the achieved height precisions for the incoherent averaging number of 1000 is lower than the ones for the incoherent averaging number of 60,000.  
Figure 3: Standard deviation vs. number of incoherent averaging for the UK-TDS-1 data. The dotted line shows the expected decrease as Equation (8).
# On Edge Pruning of Communication Networks under an Age-of-Information Framework
## 7 Pruning the Network
### Pruning in the Probabilistic Case  
In order to prune the weakest link in the network, we need to run an algorithm that identifies the least important link in the network. The definition of such a link is that it is the link in the network which when removed from the network, the total expected AoI of messages transmitted through the network increases the least.  
In order to identify such a link, the algorithm checks each link individually and when we have multiple links in parallel, they may be reduced to one link with an activation signal that is simply the summation of the activation signals of all the links. Equations (16) and (17) represent the aggregated function in the parallel case.  
When we have multiple signals added together in series, the reduced link of all of them is the activation function of the least common multiple of the time periods of the links. Equations (14) and (15) represent the aggregated function in the series case.  
**Theorem 2**.: _The link determined by Algorithm 1 is the weakest link which affects the total AoI of the network the least in the probabilistic case. The time complexity of the algorithm is \(O(|E|^{3})\)._  
```
0: G. //SP Graph.
0: Lentfying the weakest link.
0: Initialization:\(A=\{\}\). //List of AoI values.
1:for\(k\) from \(1\to|E|\)do
2: Remove the link \(e_{k}\).
3:for remaining \(e_{k}\in E\)do
4: Apply the series-parallel reduction.
5: Evaluate the new average \(\Delta\) value from Equation (18).
6: Add the new value to the list \(A\).
7: Add the link \(e_{k}\) back into the graph.
8:return the minimum value in \(A\) and the corresponding link.
```  
**Algorithm 1** Determining the weakest link in the probabilistic case  
Proof of Theorem 2.: The proof is straightforward, as the algorithm simply iterates over all the links of the network and removes them so that it evaluates the value of the average AoI with those links removed. After reporting all the AoI values with their corresponding removed links, the algorithm simply reports the link that resulted in the lowest increase in the AoI.  
We can directly show from the iterations that Algorithm 1 takes \(O(|E|^{3})\) time, which can be observed from the nested loops, each one iterates \(O(|E|)\) times. Hence, Algorithm 2 takes \(O(k\times|E|^{3})\). The flowchart in Figure 5 shows the method in a flowchart.  
```
0:\(G\), \(k\). //SP Graph and number of links to be pruned.
0:Pruning the network to reduce redundancy.
1:for\(x\) from \(1\to k\)do
2:Run Algorithm 1.
3:Prune the corresponding weakest link returned by it.
4:Obtain the new pruned SP graph \(G\)
5:Evaluate the new average \(\Delta\) value from Equation (18).
6:return the new pruned network \(G\) and its corresponding AoI.
```  
**Algorithm 2** Summary of the pruning method
# Optimal Robust LQI Controller Design for Z-Source Inverters
## 2 Circuit Analysis  
Figure 1a illustrates a three-phase voltage-fed ZSI consisting of power source, impedance network, switches, and AC load. The embedded X-shape network with two split inductors and capacitors adds an additional state to ZSIs and enables ZSIs to boost output voltage by changing the shoot-through duty cycle. Figure 1 depicts the topology of a ZSI in different switching states: the non-shoot-through mode (including six active modes when the DC voltage is impressed across the three-phase load and two conventional zero modes when the load terminals are shorted through either the lower or upper three switching devices, respectively) and shoot-through mode, during which the load terminals are shorted in seven different ways by any phase, with combinations of any two-phase legs and all three-phase legs. All these cases can be modeled mathematically using three state variables (x): the inductor current in the impedance network \(i_{L}\), the capacitor voltage in the impedance network \(v_{C}\), and the output current \(i_{o}\), i.e., x \(=[i_{L},v_{C},i_{o}]^{T}\). \(R_{o}\) and \(L_{o}\) model the inverter load, while \(r\) denotes the parasitic resistance of the inductors. Due to the symmetrical structure of the impedance network (\(C_{1}=C_{2}=C\), \(L_{1}=L_{2}=L\)), it is obvious that \(v_{C1}=v_{C2}=v_{C}\) and \(i_{L1}=i_{L2}=i_{L}\).  
The shoot-through mode is inserted only into the zero states, whereas the active states remain unchanged, and hence the AC output voltage of the inverter remains similar to a traditional inverter besides the traditional PWM techniques can be adopted with slight modification to the zero states. Figure 2 describes the simple boost PWM modulation method, w  
Figure 1: Schematic of a three-phase voltage-fed Impedance Source Inverter (ZSI) in (a) general mode, (b) non-shoot-through mode, and (c) shoot-through mode.  
vectors are evenly allocated into each phase without changing the total zero-vector time interval. Thus, the active-vector time is unchanged while the dc-link voltage is enhanced because of the introduction of shoot-through zero vectors. Here, \(f=1/T_{s}\) denotes the switching frequency in which \(T_{s}=T_{n}\) (non-shoot-through interval)\(+T_{sh}\) (shoot-through interval), and the ratio \(d=T_{sh}/(T_{sh}+T_{n})\) is called the shoot-through duty cycle.
## 5 Postfiltering Approaches for Dual-Microphone Smartphones
### Single-Channel Speech and Noise PSD Estimators  
The computation of the gain functions previously presented relies on the estimation of the single-channel PSDs of the clean speech at the reference channel \(\phi_{x_{1}}(t,f)\) and the residual noise at the beamformer output \(\phi_{v}(t,f)\). The estimation of the residual noise PSD is straightforward if estimates of the SCM \(\mathbf{\Phi}_{N}(t,f)\) and the steering vector \(\mathbf{d}(t,f)\) are available. In this case, the residual noise PSD can be obtained at each time-frequency bin as  
\[\widehat{\phi}_{v}=\left(\widehat{\mathbf{d}}^{H}\widehat{\mathbf{\Phi}}_{N}^ {-1}\widehat{\mathbf{d}}\right)^{-1}. \tag{72}\]  
On the other hand, a clean speech PSD estimate is more difficult to obtain because of its higher variability. In Reference [27], we proposed two different estimators that make use of the noisy speech and noise statistics and the RTF between microphones. In the following, we describe both clean speech PSD estimators, omitting time and frequency indices for simplicity.