esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Simulation Method  
The sketch of the proposed CMOS compatible thermopile is shown in Figure 1. Here G\({}_{\text{o}}\) is the total thermal conductance, T\({}_{\text{h}}\) is the hot junction temperature, and T\({}_{\text{a}}\) is the ambient temperature. The infrared radiation is absorbed in the active area on the front-side of a thermopile. The fabrication of the CMOS compatible thermopiles with SHA was considered as it uses the 0.35 \(\upmu\)m 2P4M CMOS-micro-electro-mechanical systems (MEMS) process in the Taiwan Semiconductor Manufacturing Company (TSMC) [16].  
In the above premise, we designed a thermopile with various SHAs by using the FDTD method. The FDTD method is an accurate and available technique to study thermopiles with SHA [13]. The sketch of the simulation model is shown in Figure 2, where \(n_{o}\) is the air refractive index and \(n_{s}\) is the SiO\({}_{2}\) refractive index. Here we considered \(n_{0}=1\) and \(n_{s}\ =1.42\). For the FDTD method, an artificial boundary condition was required to suppress reflections at the analysis windows. The perfectly matched layer (PML) (ABC) is an absorbing boundary condition and is used to truncate the computational domain without reflection [17; 18]. A perfect matched layer (PML) was applied to decrease the error caused by simulated region boundaries.  
To explore the effect of the CMOS compatible thermopile with those ESCSs in the rectangular hole of the best case in [16], we considered six ESCSs and looked for the best geometry of the ESCSs by using the FDTD method. The six ESCSs included one RC, two RCs, three RCs, one EC, two ECs, and three ECs, and the top-view sketch is shown in Figure 3. Geometric parameters of the rectangular hole taken from [16] (the best SHA case) are a hole length (in the x-axis direction) of 15 \(\upmu\)m, a hole width (in the x-axis direction) of 3.5 \(\upmu\)m, and an array thickness of 3.5 \(\upmu\)m. One can see that, based on the requirements of structure and heat conduction, we added some connection structures to connect those ESCSs to the main structure and set its value to 0.8 \(\upmu\)m. The structures can be fabricated by the etching of layers and substrates beneath the floating structures. For the rectangular column, the geometric dimensions in the x-axis and y-axis directions are Wx and Wy, respectively. For the ellipse column, the geometric dimensions in the x-axis and y-axis directions were \(D_{x}\) and \(D_{y}\), respectively.  
Figure 1: Sketch of the proposed CMOS compatible thermopile and heat conduction. SHA = subwavelength hole arrays, ESCS = extra subwavelength columnar structures.
# Research on a Bone Stick Fragment Stitching Method Based on Improved Corner Detection
## 2 Improved Corner Detection Method
### Multi-Scale and Multi-Stage Corner Detection Method
#### 2.2.2 Shi-Tomasi Corner Detection  
Then, based on the large number of initial corners obtained by the FAST corner detection algorithm, refined corner detection is required. Among the many corner detection algorithms, the traditional Harris corner detection algorithm is not easily affected by noise and image rotation. The Shi-Tomasi corner detection algorithm is an optimization of the Harris algorithm [12]. It not only avoids the phenomenon of corner clustering but also obtains more accurate results than the Harris algorithm in many cases. Therefore, the Shi-Tomasi corner detection algorithm is selected to perform more refined corner detection on all initially screened corners.  
The Shi-Tomasi corner detection algorithm mainly uses first-order partial derivatives to calculate the difference in pixel intensity after a local small window is moved in each direction at a certain pixel point, that is, the minimum eigenvalue of the gradient matrix is used to simplify the judgment of corner saliency. If the moving window in each direction at this point obtains a result with a large difference in pixel intensity, that is, the response value is greater than the set threshold \(T_{\text{Shi}}\), then this point can be considered as one of the quasi-corners. In addition, the algorithm can accurately assess the significance of each corner by calculating the response value of the corner, and can also sort the detected corners based on the response value to screen out corners of higher quality and credibility. The purpose of refined corner detection is to retain corners with a higher significance among the large number of initially screened corners in \(\text{C}_{\text{FAST}}\), especially significant corners in areas with more broken edges, and to eliminate possible false corners.  
After refined detection by the Shi-Tomasi corner detection algorithm, the number of initial corner sets \(\text{C}_{\text{FAST}}\) is usually reduced. Since the results obtained by the Shi-Tomasi corner detection algorithm are based on the response values of the corners for sorting and screening, corners with a low significance and poor quality will be removed, and the final corner set \(C_{\text{shi}}\) will be more significant. At the same time, in order to retain as many corners as possible in the broken edge area, the threshold \(T_{\text{Shi}}\) should not be set too high.
# The Applications of Magnetic Particle Imaging: From Cell to Body
## 4 Applications of MPI
### Blood Pool Imaging  
MPI generates negligible tissue background signal and has a high signal-to-noise ratio. Because of the resulting high image contrast and high sensitivity, MPI has advantages in blood pool imaging in certain scenario compared with other imaging methods that use contrasts, such as CT and MRI [40]. For instance, among the main problems in blood pool imaging is to distinguish the blood in the capillaries from the surrounding tissues. Typically, in CT angiography (CTA) and MR angiography (MRA), a minor change in the background signal is indistinguishable from a slight change in imaging contrast in capillaries [66]. However, because MPI detects only the magnetic nanoparticles in the blood and does not image surrounding tissues that do not contain magnetic nanoparticles, the device will generate images that can more easily distinguish blood and the surrounding tissues. In conclusion, MPI has positive contrast, high sensitivity, high resolution, and no limitation on penetration depth, and it can achieve fast scanning and long-term tracking. These characteristics give MPI certain advantages and  
Figure 2: The imaging results of MPI volume coregistered to corresponding CT slices allow clear visualization of tracer dynamics in rats, including initial rim enhancement followed by accumulation and clearance. Image reproduced, with permission, from Reference [8]. Copyright 2017 American Chemical Society.  
research value in the application of blood pool imaging. At present, MPI is used in the measurement of intestinal bleeding [51], brain injury [66], and cerebral blood volume [53] and has provided significant research results.  
The [PERSON] group at the University of California, Berkeley, leveraged the high sensitivity, positive contrast, and linear quantitatively of MPI to detect gut bleeding in a murine model (Figure 3) [51]. Studies showed that MPI enabled the high contrast visualization of gastrointestinal (GI) bleeding in a GI polyp development mouse model according to the aggregation of magnetic nanoparticles. In addition, because the intensity of the MPI signal was linearly correlated with the mass of magnetic nanoparticles, the device could achieve gut bleed quantification using a nonradioactive and long circulating PEGylated tracer. The [PERSON] group also explored the application of MPI in imaging traumatic brain injury [66]. Research showed that MPI could detect the initial stage of traumatic brain injury and the corresponding hematoma in the closed skull. In addition, the device could be used to determine the location, severity, and depth of bleeding of any traumatic brain injury in the closed skull. Researchers such as [PERSON] et al. of Massachusetts General Hospital used MPI to detect changes in cerebral blood volume by monitoring the concentration of SPIONs [53]. Studies have shown that MPI could detect hypercapnia at a high carrier-to-noise ratio.
# High Reliable Uplink Transmission Methods in GEO-LEO Heterogeneous Satellite Network
### Literature Review
To the best of our knowledge, the existing works did not explore the equivalent channel gain difference for users at different positions, and how NOMA behaves in terms of outage probability has not been well analyzed.
## 4 Experimental Results
### The Influence of Various Parameters
#### 4.2.1 The Influence of the Control Coefficients and the Error Signals
(**g**) Using the HILC law with \(k1=0.1\), \(k2=1\), \(k3=0.1\). (**h**) Using the HILC law with \(k1=0.1\), \(k2=1\), \(k3=1\). (**i**) Using the HILC law with \(k1=0.1\), \(k2=1\), \(k3=20\). coefficient is too small, the controller cannot effectively track the trajectory in a shorter time; if the value of the control coefficient is too large (>20), the controller cannot effectively track the given trajectory caused by increasing the error. Therefore, the system may be unstable. (2) Comparing a, d, e, g, and h in Figure 6 with different error parameters, if the error at \((k+1)\) time in the current iteration remains unchanged, the other error parameters can compensate for the error parameter to improve the convergence speed of the new-type controller. The tracking performance may be improved by increasing the other error parameter. (3) Comparing a, d, e, g, and h in Figure 6 with different error parameters and different controller coefficients, increasing the control coefficients of each error parameter can reduce the number of iterations required for the trajectory tracking by the mobile platform; however, the control coefficients of each error parameter still affect the stability of the system.
# Tempo and Metrical Analysis by Tracking Multiple Metrical Levels Using Autocorrelation
## 3 Proposed Method
### Tracking the Metrical Grid
#### 3.3.1 Principles  
The tracking of the metrical levels is done in two successive steps:  
* We first select a large set of periodicities inherent to the metrical structure, resulting in what we propose to call a metrical _grid_, where individual periodicities are called _layers_.
* We select, among those metrical layers, core metrical _levels_, where longer periods are multiple of shorter periods. Each other layers of the metrical grid is a multiple or submultiple of one metrical level. One metrical level is selected as the most prevalent, for the determination of tempo.  
For each metrical layer \(i\), its _tempo_\(T_{i}\) (meaning the tempo related to the metrical grid by tapping on that particular metrical layer) and period \(\tau_{i}\) are directly related to the tempo \(T_{1}\) and period \(\tau_{1}\) of the reference layer \(i=1\):  
\[T_{i}=\frac{T_{1}}{i},\tau_{i}=\tau_{1}i \tag{7}\]  
For instance, the tempo at metrical layer 2 is twice slower than the one at metrical layer 1. Although tempo can change over time, the ratio between tempi related to the different metrical periodicities remain constant and Equation (7) remains valid.  
The tracking of the metrical grid over time requires a management of uncertainty and noisy data. Periodicity lags measured in the autocorrelogram do not exactly comply with the theoretical lags given by Equation (7). For that reason, for each successive frame \(n\), each metrical layer \(i\) is described by both:  
* theoretically, the temporal series of periods \(\tau_{i}(n)\) related to metrical layer \(i\) knowing the global tempo given by \(\tau_{1}(n)\);
* practically, the temporal series of lags \(t_{i}(n)\) effectively measured at peaks locations in the autocorrelation function.  
In the graphical representation of the metrical structure, both actual and theoretical periods are shown: the temporal succession of the theoretical values at a given metrical layer is shown with a line of dots, whereas the actual periods are indicated with crosses that are connected to the theoretical dot with a vertical line. For instance in Figure 6, we see a superposition of metrical layers, each with a label indicated on the left side, starting from layer 0.25 up until layer 4, with also a layer 4.25 appearing around 30 s after the start of the excerpt.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 6 TWT in Wi-Fi 6
### Target Wake Time (TWT) Mechanism in Wi-Fi 6
#### 6.2.1 Overview  
The TWT was introduced in 802.11 ah [23]. It has been adopted by Wi-Fi 6 to reduce energy consumption and solve contention issues. TWT aims to introduce a deterministic and collision-free wireless network. It reduces the latency and queue occupancy by utilizing multi-user transmissions and packet aggregation. In addition, the idle time of the stations increases, which considerably reduces energy consumption [13]. TheTWT balances the load with different TWT sessions for different stations. It also decreases the medium access contention by frame exchanges in advance. The standard does not specify a default scheduling for the TWT and is manufacturer-specific.
## 3 Results
### Self-Reported Outcome Measures  
The followup survey showed that 11 stroke patients (84.6%) and 17 healthy young adult subjects (94.4%) observed a difference in pedalling with feedback or engagement relative to pedalling without (Table 2). Of the stroke patients, two subjects (15.4%), eight subjects (61.5%), one subject (7.7%), and two subjects (15.4%) found feedback during pedalling to be really enjoyable, enjoyable, neutral or slightly troublesome, respectively. When engagement was provided, the number of stroke patients that enjoyed and really enjoyed pedalling increased to three (23.1%) and nine (69.2%), respectively. Eleven stroke subjects (84.6%) believed both feedback and engagement to be beneficial to their exercise routine, and 12 stroke subjects (92.3%) felt that feedback and engagement would help to motivate them to adhere to pedalling-based exercise. All stroke participants indicated they would choose to pedal with engagement in the future if given the opportunity, and 11 patients (84.6%) would elect to pedal with feedback.  
Of the healthy subjects, 16 (88.9%) believed that pedalling with feedback and engagement would assist them with their exercise, while 16 subjects (88.9%) and 17 subjects (94.4%) believed that feedback and engagement would help motivate them to adhere to pedalling exercise, respectively. Fourteen healthy adult subjects (77.8%) would perform pedalling with video-based engagement if given the opportunity, and 15 subjects (83.3%) would elect to undertake pedalling with feedback. Written feedback from the survey indicated that the majority of stroke patients (81.8%) and healthy adults (82.4%) found feedback helpful or useful in their performance tracking, while most stroke patients (92.3%) and healthy adults (70.6%) found engagement both relaxing and appealing as an exercise therapy.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c} \hline  & & \multicolumn{8}{c}{**Healthy Adults**} & \multicolumn{8}{c}{**Stroke Patients**} & \multicolumn{8}{c}{**Stroke Patients**} & \multicolumn{8}{c}{**Intercalation Effect**} \\ \cline{2-13}  & & & \multicolumn{2}{c}{**Baseline**} & \multicolumn{2}{c}{**Feedback**} & \multicolumn{2}{c}{**Engagement**} & \multicolumn{2}{c}{**Baseline**} & \multicolumn{2}{c}{**Feedback**} & \multicolumn{2}{c}{**Engagement**} & \multicolumn{2}{c}{**Individual Effect**} & \multicolumn{2}{c}{**Intercalation Effect**} \\ \cline{2-13}  & **Calence** & **Mean** & **SD** & **Mean** & **SD
# A Consensus-Based Likert-LMPP Model for Evaluating the Earthquake Resistance of Existing Buildings
## 3 Methodology
### Building Vulnerability Parameters (Inputs to the Proposed Model)
#### 3.3.8 Structural System States  
Different types of buildings are more or less vulnerable to earthquakes. Structural load-carrying systems determine a building's lateral strength and ductility during a seismic event. Several parameters affect this parameter, including structural geometry, member dimensions, and design quality. It was stated in the Ministry of Environment and Urban Planning's (2019) method for evaluating existing reinforced concrete buildings of 1 to 7 floors that the buildings could have reinforced concrete frames (RCF) or reinforced concrete frame and curtain (RCFC) systems for load bearing. As stated, the load-bearing system should be determined from the inside of a basement floor if there is one, or from inside a store if there is one, or if it cannot be determined, the RCF should be assumed to be ineffective or zero.
# Spaceborne Sparse SAR Imaging Mode Design: From Theory to Implementation
## 5 Conclusions  
In this paper, the design scheme of a sparse SAR imaging mode is described in detail. It combines the advantages of traditional imaging modes and sparse signal processing. Compared with traditional modes, the designed sparse modes utilize downsampled beam positions and adopt a sparse imaging method, achieving wider swath coverage, reducing data volume, and lowering RASR without hardware changes. The used \(L_{2,q}\)-norm regularization-based sparse SAR imaging method can obtain unambiguous images of large-scale areas with reduced PRF, which makes it possible to achieve high-quality data processing in spaceborne sparse SAR systems. Sparse SAR imaging performance relies on prior scene knowledge, generally assuming sparsity. Thus, it should be noted that the proposed design scheme of sparse SAR imaging modes is more applicable to the observation of sparse surveillance areas.  
Methodology, Y.S., F.C. and J.Z.; Validation, H.B., G.L. and W.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 62271248, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20230090, and in part by the Key Laboratory of Land Satellite Remote Sensing Application through the Ministry of Natural Resources of China under Grant KLSMNR-K202303.  
Not applicable.  
Data Availability StatementData is contained within the article.  
The authors declare no conflicts of interest.
## 3 Results
### Datasets  
In order to better compare the performance of the proposed method and baseline model, the Qijiang Basin in Chongqing, China was selected as the experimental target basin, and relevant measurement data from the Qijiang Basin in Chongqing were used for the experiment. The flow data used in the experimental watershed come from hydrological stations within the watershed, including six rainfall stations (Wucha, Caijia, Shijiao, Xinlu, Dongxi, and Yangshi) and one flow station (Wucha). Figure 7 shows the distribution of stations in the experimental watershed. From the graph, it can be seen that there is a certain spatial correlation between the upstream and downstream stations in the watershed. The meteorological data used in the experiment were from mobile phones and the National Meteorological Information Center of China. Specific types of meteorological data include evaporation data, rainfall data, temperature data, and wind speed data. The input for the experiment includes previous data from 1 to 12 days (including the current). The output is runoff data for the next 1-6 days. The time span of the Qijiang River Basin dataset is from 1 January 1979 to 31 December 2020. The data granularity is one item per day. Due to the fact that the above data usually follow a relatively clear data distribution, in order to better partition the dataset while maintaining the data distribution, this study used a random shuffling method to shuffle the original dataset to approximate the random distribution. Then, through linear partitioning, the original dataset is divided into training, validation, and testing sets in different proportions.
# Path Tracking of Agricultural Vehicles Based on 4 WIS-4 WID Structure and Fuzzy Control
## 3 Results and Discussion
### Analysis and Discussion  
#### 3.2.1 Path Tracking Accuracy  
From Figure 12 and Table 3, it can be obtained that the average deviation of TPP under four different initial states are 620 mm, 220 mm, 277 mm, and 342 mm, respectively, which are significantly higher than 169 mm, 153 mm, 212 mm, and 38 mm of FCP. The mean value of average deviation is affected by the initial state, but the FCP path-tracking method proposed in this paper has higher accuracy than the TPP method. Nevertheless, the effect of the FCP method on tracking accuracy is more evident in the state of significant deviation. When the vehicle reaches the steady state, the steady-state deviations of FCP in four different initial states are 46 mm, 35 mm, 36 mm, and 51 mm, which are less than 100 mm and can meet the demand for high-precision path tracking in narrow environments such as greenhouses.  
Figure 11: Test site and base station information.
## Appendix A The Time Frequency Distribution of the 4-QAM Modulated Signal  
In a single carrier pulse shaping system, the frequency deviation \(\
u(t)\) is determined by \(I(t)\) and \(Q(t)\), where \(I(t)\) and \(Q(t)\) are associated with \(M\) continuous transmitted data. Therefore \(I(t)\) and \(Q(t)\) have \(2^{2M}\) kinds of combination, which makes the signal have \(2^{2M}\) kinds of time-frequency energy distributions. It is considered that the pulse shaped waveform of each symbol is mainly affected by its two neighbor symbols, thus \(M=3\). Therefore, there are 64 kinds of energy distributions for different \(I(t)\) and \(Q(t)\). However, by simulation, it is found that some of them are the same, and finally there are totally 16 kinds of energy distributions. Figure 5 illustrates two of them, where the continuous three symbols for Figure 5a are \([-1-i,-1+i,-1-i]\), and the continuous three symbols for Figure 5c are \([1-i,1-i,1+i]\).  
In Figure 5a,c, the red contours represent the high energy area, and the dark blue contours represent the low energy area. It shows that the signal's energy concentration varies with time. Figure 5b is the frequency deviation \(\
u(t)\) of signal \(s(t)\) when the transmitted symbols are \([-1-i,-1+i,-1-i]\). It shows that the variation of \(\
u(t)\) is in accordance with the energy center  
Figure 5: Time-frequency distribution and frequency deviation of 4-QAM modulated pulse shaping signals. **(a)** The time frequency distribution of \(\{-1-i,-1+i,-1-i\}\); **(b)** The frequency deviation of \(\{-1-i,-1+i,-1-i\}\); **(c)** The time frequency distribution of \(\{1-i,1-i,1+i\}\); **(d)** The frequency deviation of \(\{1-i,1-i,1+i\}\).  
of the signal's time frequency distribution, which is shown in Figure 5a. This verifies the physical analysis of (15). According to Figure 5a, the allocated bandwidth is not occupied by signal \(s(t)\) all the time. In the dark blue and blank areas, the desired signal's energies are very low, but the interference and noise are not negligible. Hence if the signal is filtered according to its energy distribution, more interference and noise could be filtered out while maintaining most of the desired signal's energy.  
In summary, the pulse shaped quadrature modulated signal also has a time-varying spectrum, but it has no clear regularity. If apply the time-varying filter to the quadrature modulated signal directly, it would be a challenge to design the pass region of the time-varying filter. In [34], we discussed the performance improvement of the time-varying filter with perfect pass region, but it is not practical since the perfect pass region of the QAM modulated signal couldn't be obtained at the receiver.