esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Monitoring Program and Results
### Monitoring of Soil Displacement
#### 5.2.1 Surface Subsidence Analysis  
The surface deformation brought on by shield excavation can be separated into four stages based on the excavation procedure and deformation mechanism of shield construction: Stages 1 and 2's surface deformation occurs in front of the cutter plate, the soil in front of and above the excavation is extruded and deformed by the shield's control of soil pressure (mud and water pressure), and the surface appears to be uplifted; Stage 3 occurs when the shield passes through the excavation surface, and ground subsidence occurs due to over-excavation, deviation correction, and friction between the periphery and the surrounding soil; Stage 4 occurs after the shield passes through the excavation surface,  
Figure 12: Comparison and verification of numerical simulation and measured results. (**a**) comparison and verification of numerical simulation and measured results, (**b**) radial deformation of pipe section.  
Figure 13: Monitoring point layout drawing.  
and surface deformation is caused by soil consolidation and subsidence, which is mainly controlled by grouting.  
The time curve of uplifting and settlement for the measurement locations D1 to D9 is shown in Figure 14, where a positive value denotes uplifting, and a negative value denotes settlement. Each measuring point's deformation may undergo a process of bulging and then settling. After pipe jacking I was completed on January 6, and the construction of pipe jacking II began, the curve once more displayed an uplifting trend. According to the monitoring data, the maximum ground settlement is larger than the simulation result, which may be due to the untimely filling of the gap between the back of the lining and the cave body after the shield passed through the section, resulting in the release of the ground stress and causing ground settlement.
# A Survey on Memory Subsystems for Deep Neural Network Accelerators
## 6 Processing-in-Memory (PIM)
A neuron consists of 128 column-shaped multiply and accumulate units (column MACs) arranged in a row. A bitcell array, dependent on precision settings, can be manipulated to adapt to various N bit column MACS up to 16-bit precision. An advantage this architecture holds over conventional processing methods is the number of components required to achieve the desired bit precision. In conventional arithmetic logic, the multiplier size grows quadratically as bit precision increases. However, this architecture's multiplier size grows in proportion to the bit precision. On a 65 nm test chip, the energy efficiency is reported as 117.3-2.06 TOPS/W for 1- to 16-bit precision respectively. Summary and Discussion  
This segment of the article examines processing-in-memory (PIM) which is another promising memory architecture used in DNN accelerators. Though PIM permits faster communication between the processor and memory, high-precision achievement has been a challenge. Recent works show that floatPIM enables block size scalability with its digital storage capabilities. A comparator-based PIM architecture offers rich scope for fast and efficient neural networks. Another work illustrates that parallel access of memory subarrays in PIM promises an upsurge in energy efficiency. A combination of fixed-function logic and programmable cores in heterogeneous processing-in-memory also supports fast data communication.
# Adaptive Pitch Control of Variable-Pitch PMSG Based Wind Turbine
## 2 Model and Problem Formulation
### Pitch Control  
To maintain the extracted wind power at rated power in Region 3, it requires that the corresponding pitch angle should be achieved, which in turn requires both the mechanical rotation speed \(\omega_{\rm m}\) and the mechanical torque \(T_{\rm m}\) should be kept at their rated values, respectively. The rated mechanical torque \(T_{\rm mr}\) is achieved when the electromagnetic torque \(T_{\rm e}\) can track its rated value \(T_{\rm er}\) and the \(\omega_{\rm m}\) is kept at it rated value. According to Equation (7), the electromagnetic torque \(T_{\rm e}\) can be maintained at \(T_{\rm er}\) if the q-axis stator current \(i_{\rm mq}\) can track its rated value \(i_{\rm mqr}\) and \(i_{\rm md}\) is kept at 0.  
The brief overall control approach is shown in Figure 3. The control approach consists three controllers: two stator current controllers and a rotation speed controller.
# Automated Defect Detection Using Threshold Value Classification Based on Thermographic Inspection
## 4 Results and Discussion
### Filtering  
After calculating the optimum frequency for the first de-noising, filtering was applied to each image. The applied filtering was mean, median, NLmean and Gaussian. A total of five images, which were the filtered and non-filtering images, were compared and analyzed. The SNR was calculated to evaluate the improvement of the defect detectability of the de-noising image. The SNR is defined as the ratio of the signal strength to the noise strength that disturbs the signal. The defect area was considered as'signal' and the sound area as 'noise'. The ROI area was the same as the area where the contrast evaluation was performed, and the SNR equation is as follows [28; 35; 36]:  
\[\mathrm{SNR}=20\log_{10}\left(\frac{\left|\mathrm{DROI}_{\mathrm{mean}}- \mathrm{SROI}_{\mathrm{mean}}\right|}{\sigma}\right) \tag{8}\]  
where \(\mathrm{DROI}_{\mathrm{mean}}\) and \(\mathrm{SROI}_{\mathrm{mean}}\) are the arithmetic mean of all the pixels in the defective area and the sound area, respectively, and \(\sigma\) is the standard deviation of all the pixels in the sound area.  
Figure 8 shows the non-filtering raw image and the filtering image. Based on the raw image, it can be seen that the Gaussian image and the NLmean image have significantly lower detectability. However, it is difficult to qualitatively evaluate improvements in the median image and mean image. Therefore, quantitative detectability was evaluated using the SNR equation of Equation (8); Figure 9 shows the SNR value of each image. Both amplitude and phase signal processing had the highest detectability in median images. In addition, it can be seen that the detectability was improved compared to the raw image to which non-filtering was applied.  
Figure 7: Plot of ROI contrast for optimum excitation frequency: (**a**) amplitude and (**b**) phase.
## 2 Materials and Methods
### Data Adjustment Techniques  
#### 2.4.1 Data Adjustment via Response Surface Methodology  
The response surface methodology (RSM) is a set of mathematical techniques used to identify the influence of quantitative factors on a response variable. To obtain the correlation between the values of the concentration of TRS and input parameters such as the particle size of the mesquite sample, the hydrolysis time, and the sulfuric acid concentration, the modeling of the data gathered from the experiments was carried out by RSM. The average of the experimental results is shown in Table 1. Comparisons of the results from the different treatments were performed by ANOVA for a 3\({}^{\mathrm{k}}\) design in which the variables were coded as \(-1\), 0, and +1 for the various levels, using the Design Expert 13 software. A mathematical model was created for the variables' response, based on the quadratic model of the response surface shown in Equation (1).  
\[f=\beta_{0}+\sum_{i=1}^{k}\beta_{i}x_{i}+\sum_{j=2}^{k}\sum_{i=1}^{j-1}\beta_{ ij}x_{i}x_{j}+\sum_{i=1}^{k}\beta_{ii}x_{i}^{2}+e \tag{1}\]  
where \(\beta_{0}\) is the equation constant, \(\beta_{i}\) is the linear term coefficient, \(\beta_{ij}\) is the variable interaction term coefficient, \(\beta_{ii}\) is the quadratic term coefficient, \(x_{i}\) and \(x_{j}\) are the independent variables, and \(e\) represents the model error.  
The quadratic model allows for the examination of both linear and curvature effects in the relationship between the independent variables and the response. The intercept (\(\beta_{0}\)) is the value of the response when both independent variables are zero. The linear terms (\(\beta_{i}\)) capture the linear effects, while the quadratic terms (\(\beta_{ii}\)) account for curvature in the response surface. The cross-product coefficient (\(\beta_{ij}\)) represents the interaction between the two independent variables; it shows whether there is a combined effect that is not explained by the individual effects. These coefficients are determined through a regression analysis of experimental data. The experimental design often involves running a set of experiments with different combinations of input variables and measuring the corresponding response variable.
## 2 THz-SAR Echoes Signal Model with Vibration Error  
### High-Frequency Vibration Error Model of Platform  
In SAR imaging, besides the translational motion error, the vibration error of the motion platform is also inevitable because of the effects of air flow and engine vibration. Compared with traditional translational motion error, the motion platform's vibration is of the higher vibration frequency that usually meets the conditions.  
\[|f\cdot T_{s}|\geq 1 \tag{1}\]  
where \(T_{s}\) is the synthetic aperture time of SAR.  
So far, the vibration of motion platform is usually modeled as a simple harmonic vibration, which is expressed as  
\[\Delta R(t)=a\sin(2\pi ft+\phi) \tag{2}\]  
where \(t\) represents the azimuth time, \(a\) denotes the vibration amplitude, \(\phi\) represents the initial phase, and \(f\) is vibration frequency.  
However, in reality, the high-frequency vibration of the motion platform is complex and modeled as a superposition of multiple harmonic vibration. Therefore, to characterize the motion platform vibration more accurately in this paper, we model it with the form of  
\[\Delta R(t)=\sum_{i=1}^{l}a_{i}\sin(2\pi f_{i}t+\phi_{i}) \tag{3}\]  
where \(I\) represents the number of vibration components, \(a_{i}\), \(f_{i}\), and \(\phi_{i}\) denote the vibration amplitude, vibration frequency and initial phase of the \(i\)'th vibration component.
## 4 Performance Evaluation  
In this section, we evaluate the efficiency of our proposed Friendly-Sharing approach. As commented previously, the performance of the message diffusion will mainly depend on the mobility of the users and the number of contacts and their duration between pair of users. The impact of the dropping and forwarding policies in the local buffer is also evaluated, in order to select the best policy.  
We employed the ONE simulator [16] using a real movement trace of mobile users. This trace comes from an experiment at the NCCU campus [17], where GPS position data was collected duringtwo weeks (336 h) using an Android app installed in the smartphones of 115 students. Figure 3 shows a snapshot of the ONE running with the corresponding graphical map information from NCCU.  
The workload considered tried to mimic the typical data-flow for a multimedia messaging application where shorter messages are far more common than larger ones. Three message sizes and frequencies were considered: (1) a short text message (1 kB) every hour; (2) a photo (1 MB) every 18 h; and (3) a video or high-resolution picture (10 MB) every 96 h. These frequencies were based on the statistics of Whatsapp (Facebook, Inc., Menlo Park, CA, USA) message usage from [18], while sizes are approximations of the content produced by current mobile phone hardware.  
The communication range (\(r\)) was set to 7.5 m with bandwidth \(B_{w}=2.1\) Mb/s. These assumptions allow taking into consideration the necessary set-up time of connections and the possible impact of interferences. These values are based on the Bluetooth 2.0 class 2 specifications. Other technologies such as WiFi Direct are not considered to avoid issues like disconnecting the device from the internet. WiFi is increasingly being used for data connectivity, especially at large enterprise areas or university campuses, and it would be a nuisance for users to deal with these disconnection periods.
## 4 Conclusions  
This article proposes a new method that combines the multi-strategy improved TSO and INC methods to track the GMPP of a PV system. The proposed new method performs MPPT tracking by optimizing the converter duty cycle. In the simulation, the PV system was composed of PV array, boost converter, and an MPPT control module. The results were obtained by comparing the proposed ITSO-INC with INC, CS, and TSO through the MATLAB simulation platform. Under three different shadow conditions, ITSO-INC had good tracking speed, robustness, and tracking efficiency. ITSO-INC had good performance under constant and variable PSCs. Compared with TSO and INC, the tracking efficiency of the new method increased by 7.39% and 4.48% under pattern 4. In terms of MPPT tracking rate, ITSO-INC was greatly improved compared with the other three methods. Under the condition of variable PSCs, the new method increased the tracking rate by 26.89% and 60.29%. These results show that ITSO-INC is a competitive and robust high-speed tracking method. Therefore, it can be concluded that ITSO-INC gives the best solution to track MPP for any sort of irradiation changes.  
Conceptualization, Y.L.; methodology, L.L.; validation, Y.J., J.Z., Y.G. and S.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by key projects of the East University of Heilongjiang (HDFKY200104) and general projects of the East University of Heilongjiang (HDFKY210106).  
Figure 12: The obtained duty cycle by the algorithms over the step changes from pattern 3 and pattern 4. (**a**) TSO; (**b**) ITSO; (**c**) CS; (**d**) INC.
# Surface Subsidence Monitoring in Kunming City with Time-Series InSAR and GNSS
###### Abstract
Kunming city is located in Bianchi basin, where surface subsidence caused by excessive extraction of confined water and large-scale construction has attracted much attention. Therefore, accurate acquisition of surface subsidence in Kunming city is particularly important for understanding the complex geological environment. Most of the previous literatures focused on the surface subsidence obtained from single data in Kunming city [32; 33], while few inversion combined with GNSS data for large-scale surface subsidence. In this paper, 35 Sentinel-1A descenting orbit data covering Kunming city from March 2018-December 2020 are obtained, and the surface subsidence information of Kunming city during this period is extracted by time series processing. At the same time, the data of 8 Continuously Operating Reference Stations (CORS) in this area during January 2019-December 2020 are combined. The GAMIT/GLOBK software is used to solve the problem. The projection results of SBAS-InSAR based on GACOS and CORS in the direction of radar line of sight are compared, and the time series comparison results of the same period are analyzed. GNSS monitoring data are used for comparative analysis to support the reliability of the results obtained by InSAR technology. Therefore, under the complex background environment of large area, urban area combined with mountain area, dense vegetation, excessive exploitation of groundwater and wide distribution of wetland water body in Kunming City, this paper tries to apply SBAS time series analysis technology combined with GNSS technology to land subsidence monitoring in Kunming City, and compares and verifies the observation results, so as to provide reference for land subsidence monitoring under complex background.
## 5 Analysis of Roughness Detection Method by Kernel  
### Analysis Method  
In this paper roughness detection method by kernel was applied to estimate the three-dimensional roughness. In this method, a point cloud is projected onto the kernel and the roughness of the sample is determined by the elevation of points in the kernel. Since the three-dimensional roughness is affected by the size of the kernel measuring the roughness, the optimized kernel radius which can reflect the roughness of three samples  
Figure 8: Distance distribution between the fitting plane and point clouds.  
Figure 9: Normalized histogram for three samples.  
was evaluated by the roughness analysis of changing the kernel radius. Equation (1) shows the roughness ([PERSON], 2018).  
\[R_{j}=(|\Delta Z_{1}|+|\Delta Z_{2}|\ldots|\Delta Z_{i}|)/\text{n} \tag{1}\]  
where, \(R\) is roughness calculated for points within a kernel radius of \(j\), \(\Delta Z\) is the difference in elevation from a measurement point and the least squares plane calculated by the nearest neighboring measurement points, and n is the number of measurement points within a kernel radius (see Figure 10a). As shown in Figure 10b, the roughness was analyzed by changing the kernel radius to 0.01, 0.025, 0.05, and 0.1 m. The maximum kernel radius was set as 0.1 m because the diameter of the sample is 0.1 m and the longest length of the failure surface is about 0.2 m. The minimum kernel radius was set as 0.01 m considering that the diameter of the gravel is 0.02 m. When the kernel radius increases, the local roughness is diluted by other points. When the kernel radius decreases, it is difficult to reflect the global curvature of roughness. Therefore, this paper evaluated the optimized kernel radius that can best reflect the roughness of the three samples.
## 1 Introduction  
Patients with Class III malocclusion with mandibular pragnathism have a high prevalence of facial asymmetry [1, 2, 3, 4]. The prevalence of skeletal Class III face asymmetry has been reported to range from 47.9% to as high as 85% in Asian cohorts [3, 4]. The lower face, particularly the chin, deviates more frequently than the upper face [2, 3]. The occurrence of facial skeleton asymmetry may result from variations in the morphology of bony structures and the positional deviation of the maxillomandibular complex, or only the mandible [5, 6].  
For patients with severe skeletal jaw discrepancy and facial asymmetry, a combination of orthodontic treatment and orthongathic surgery (OgS) is considered the only viable treatment option for improving facial appearance and restoring normal occlusal function [5]. The goal of 2-jaw OgS is to correct the overall dentofacial asymmetry regarding dental occlusion, align maxillary midline and chin to the facial midline, level the oral commissure and occlusal plane cant, and correct vertical and sagittal discrepancy [6]. Adjunctive surgeries or treatments, such as bone shaving, bone grafting, and soft tissue grafting, can improve contour asymmetry and enhance harmony of the face form [7, 8]. With the development of 3D images, cone-beam computed tomography (CBCT), and computer-assisted surgical simulation, 2-jaw OgS can be precisely planned, assisting surgeons to achieve similar treatment outcomes to correct facial asymmetry [9; 10; 11].  
In many cases, however, residual asymmetry persists postoperatively [12]. [PERSON] et al. reported that mandibular asymmetry persisted in 45 patients with facial asymmetry undergoing OgS, even though the center point of the mandible was well aligned with the facial midline [13]. According to the previous study from our team, [PERSON] et al. analyzed 24 patients with skeletal Class III malocclusion with double-jaw OgS, who were classified according to the outcome of subjective visual perception scores [14]. The findings demonstrated that midline parameter deviation, shape of the mandibular border, and contour of menton morphology influenced the visual perception of postoperative asymmetry.  
[PERSON] et al. categorized mandibular asymmetry into three groups according to the amount and direction of ramus asymmetry relative to menton deviation in 3D-CBCT analysis [15; 16]. They concluded that ramus asymmetry was less predictable. However, no specific factors were found to contribute to residual facial asymmetry.  
The study aims to explore the preoperative anatomical variables affecting the outcome of surgical correction in facial asymmetry in patients with skeletal Class III malocclusion. The null hypothesis was that the anatomical factors are not different between the surgical outcome of good facial symmetry and residual asymmetry.
# Acupressure for Managing Osteoarthritis: A Systematic Review and Meta-Analysis
## 4 Discussion
### Potential Biases in the Review Process  
The main limitation of this review is the small number of studies with small sample size, limiting the number of studies included in the meta-analysis. We included the statistical analysis of acupressure as compared to sham acupressure. However, the designs of sham acupressure are yet to be clearly defined in clinical trials. The designs of sham acupressure in the studies are described as the manipulation of points selected away from the real acupoint with gentle pressure in one trial [38], manipulation of points selected near the real acupoints of similar patterns without pressure in one trial [39], and manipulation of points that were not on meridians with pressure in another trial [40]. Different designs of sham acupressure may more or less produce placebo effects, affecting the evaluation of the true acupressure effect. Therefore, the significance of our results on the comparison of acupressure with sham acupressure should be interpreted carefully.  
Moreover, three RCTs [41; 43; 44] did not register their trials where pre-specified analysis intentions for the trial cannot be acquired. Of the four RCTs [38; 39; 40; 42] that registered their trial, the reported results for the outcome domain in one trial [40] mostly correspond to intended outcome measurements, except for one additional scale were reported for pain assessment and one intended outcome is missing from the reported results, resulting in selective reporting bias. Due to the characteristics of acupressure, two RCTs [41; 43] also used an open-labeled design where the trial is non-blinded; therefore, increasing their performance bias. As we included all studies relevant to our review question, it is probable that our findings may be overstated and the evidence may be distorted.
# Coordinated Control of Virtual Power Plants to Improve Power System Short-Term Dynamics
## 4 Case Study
### Monte Carlo Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Statistics** & **Mode 1** & **Mode 2** & **Mode 3** \\ \hline \multirow{4}{*}{\begin{tabular}{c} \(\mu_{\text{CQI}}\) \\ \(\sigma_{\text{CQI}}\times 10^{-5}\) [pu(Hz)] \\ \end{tabular} } & 1.001431 & 1.001511 & 1.002144 \\  & \(\sigma_{\text{CQI}}\times 10^{-5}\) [pu(Hz)] & 23.6 & 28.1 & 9.73 \\ \hline Control Type & Statistics & Mode 4 & Mode 5 & Mode 6 \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Prop. Control \\ \end{tabular} } & \(\mu_{\text{CQI}}\) [pu(Hz)] & 1.000328 & 1.000323 & 1.000321 \\  & \(\sigma_{\text{CQI}}\times 10^{-5}\) [pu(Hz)] & 1.83 & 1.58 & 1.43 \\ \multirow{2}{*}{
\begin{tabular}{c} Lead-Lag \\ \end{tabular} } & \(\mu_{\text{CQI}}\) [pu(Hz)] & 1.000198 & 1.000155 & 1.000135 \\  & \(\sigma_{\text{CQI}}\times 10^{-5}\) [pu(Hz)] & 8.62 & 2.24 & 3.27 \\ \multirow{2}{*}{PI} & \(\mu_{\text{CQI}}\) [pu(Hz)] & 1.000267 & 1.000020 & 1.000023 \\  & \(\sigma_{\text{CQI}}\times 10^{-5}\) [pu(Hz)] & 12.4 & 1.37 & 1.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean frequency \(\mu_{\text{CQI}}\) and standard deviation \(\sigma_{\text{CQI}}\) for different VPP control modes. Figure 6: Trajectories of the frequency of the Center of Inertia (COI) without VPP frequency control and without ESS. The mean and the standard deviation of the frequency at \(t=50\) s are \(\mu_{\text{CQI}}=1.002\) pu(Hz) and \(\sigma_{\text{CQI}}=6.16\cdot 10^{-5}\) pu(Hz), respectively. Figure 7: Trajectories of the frequency of the COI without the proposed coordinated control. used to regulate the frequency, the PI controller leads more often the ESS to its maximum power output, thus making the control less efficient as with the proportional controller and lead-lag. All controllers perform well for Mode 6, i.e., when a timer shifts the action of the DERs. The timer in fact exploits the ability of the ESS to regulate the frequency in the first seconds but avoids ESS saturations in the longer term.