esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Performance Analysis and Experimental Study of Four-Star-Type Crank-Linkage Mechanism
## 2 Numerical Calculation of Crank Linkage
### Modal Analysis of Four-Star-Type Crank Linkage
#### 2.4.1 Modal Equations  
The differential equation for vibration is as follows:  
\[[M]\Big{\{}\ddot{X}\Big{\}}+[C]\Big{\{}\dot{X}\Big{\}}+[K]\{X\}=\{F\} \tag{16}\]  
where \([M]\), \([C]\), and \([K]\) are the mass, damping, and stiffness matrices, respectively. \(\Big{\{}\ddot{X}\Big{\}}\), \(\Big{\{}\ddot{X}\Big{\}}\), and \(\{X\}\) are the acceleration, velocity, and displacement matrices of the reference point, respectively. \(\{F\}\) is the load matrix.  
The effect of damping \([C]\) on the mode shape and modal frequency is negligible, and for the free modal analysis, we utilize \(\{F\}=\{0\}\), and we obtain the following:  
\[[M]\Big{\{}\ddot{X}\Big{\}}+[K]\{X\}=\{0\} \tag{17}\]  
When the structure is vibrating freely at the intrinsic frequency, let \(\{X\}=\{A\}\sin(\omega_{i}t+\varphi)\), and we obtain the following:  
\[\Big{\{}[K]-\omega_{i}^{2}[M]\Big{\}}\{A\}=\{0\} \tag{18}\]  
Figure 8: Stress distribution nephogram of the secondary linkage assembly.  
Figure 7: Stress distribution nephogram of the secondary linkage.  
The equation has a non-zero solution; then, \(\left|[K]-\omega_{t}^{2}[M]\right|=0\). The characteristic root \(\omega_{0}\) is the intrinsic frequency of the structure. The characteristic vector \(\left\{A_{j}\right\}\) is the corresponding vibration mode. As some of the structures are irregular in shape, it is difficult to find the analytical solution using the differential equation. The solution is usually performed by the finite element method and verified by experiments.
# Improved First-Order Motion Model of Image Animation with Enhanced Dense Motion and Repair Ability
## 3 Experiments and Results
### Experimental Results and Analysis  
#### 3.3.1 Reconstruction Quality  
Quantitative reconstruction results are shown in Table 1. From the results in Table 1, we can see that our method achieved better results than FOMM with almost all indicators on all the three datasets. Especially on the TaiChiHD dataset, \(\mathcal{L}_{1}\), AKD and MKR decreased by 4.9%, 13.5% and 25.8%, respectively. In addition, on VoxCeleb1 for face movement transfer, \(\mathcal{L}_{1}\), AKD and AED decreased by 6.5%, 5.1% and 0.7%, respectively.
# Improved PR Control Without Load Current Sensors and Phase-Locked Loops for APFs
## 5 Experimental Verification
### Analysis of Proposed PR Control Experiment  
Figure 12 shows the current and voltage waveforms of the grid under the PR control algorithm proposed in this paper under steady-state conditions. Figure 12a shows the grid phase A voltage and grid phase A current. It can be seen that the compensated grid current operates at unity power factor. Figure 12b shows the three-phase current waveform, which has good overall symmetry, greatly reduced ripple, and significantly weakened harmonic components. Figure 12c shows the steady-state current FFT, and the THD is reduced to 4.0%, which is 2.2% lower than the traditional method, verifying the enhanced harmonic suppression capability. This result shows that under the steady state of the grid, the proposed improved PR control algorithm can guarantee the power quality to a certain extent.  
To further verify the dynamic performance of the control strategy, this study drops the load from 120 \(\Omega\) to 60 \(\Omega\) at t = a to simulate the load mutation. Figure 12d shows the grid current and load current waveforms of the load mutation under the algorithm proposed in this paper. At the moment of mutation, the load changes from about 120 \(\Omega\) to 80 \(\Omega\). The current does not overshoot at the moment of disturbance. The response process is smooth and oscillatory. The current waveform quickly tends to the new steady state, reflecting the high adaptability and strong robustness of the controller to the mutation conditions, effectively avoiding the risk of triggering the protection device due to overshoot current in traditional APF, and significantly improving the operating reliability of the system under dynamic conditions.  
While the traditional PR control achieves basic compensation performance, it requires multiple harmonic-specific PR controllers, a PLL, and load-side current sensors to realize real-time control. In contrast, the proposed method eliminates PLL and load-side current sampling and can achieve precise regulation only through grid-side current closed-loop control, greatly simplifying the control structure. It does not require multiple sets of harmonic compensation PR controllers, reducing implementation complexity and computing resource consumption.  
Figure 12: Steady-state situation of proposed PR control: (**a**) phase A grid voltage and current; (**b**) grid current; (**c**) steady-state current FFT; (**d**) phase A grid voltage, phase A grid current, and phase A load current.
# Wireless Power Transfer Approaches for Medical Implants: A Review
## 4 Conclusions  
This research has evaluated and discussed a survey of the following popular methods for wirelessly transferring power into IMDs: (1) inductive-based WPT, (2) microwave-based WPT, (3) ultrasound-based WPT, and (4) hybrid wireless power transmission (HWPT). In this research, the power delivered in the reviewed works to medical implants varied from a few uW to 5.4 W, with distance ranges from 1 mm to 4 m and maximum efficiency of up to 95%. Based on collected papers' research, it was concluded that ultrasound-based and inductive-based WPT works at low operating frequency (less than 50 MHz), whereas the microwave-based WPT typically works at a higher frequency. On the other hand, the HWPT could be found at a low or high operating frequency, depending on the combination used. It can be seen that the receiver dimension was less than 30 mm for all the WPT-based methods. Furthermore, HWPT had a bigger receiver size. The maximum power transfer efficiency was conducted via inductive-based WPT. Based on collected papers, the value of achievable maximum efficiencies were 95%, 78%, 50%, and 17% for inductive-based, microwave-based, ultrasound-based, and HWPT, respectively. This paper provides a perspective on different WPT approaches for biomedical applications by investigating the significant works in this field. Additionally, some points for designing effective IMDs and related commercial rules and regulations are presented.  
Conceptualization, M.H. and R.S.; methodology, M.H. and R.S.; investigation, M.H. and R.S.; resources, M.H.; writing--original draft preparation, M.H.; writing--review and editing, M.H. and R.S.; visualization, M.H. and R.S.; supervision, M.H.; project administration, M.H. All authors have read and agreed to the published version of the manuscript.  
The financial support of the School of Electrical Engineering and Computer Science, University of North Dakota, is acknowledged.  
The authors declare no conflict of interest.
## 4 Results
Following the previously outlined methodology, considering the active power imbalance per unit (p.u.) and the CoI RoCoF using Equation (18), the inertia values of the power system were estimated depending on the level of RES penetration. Table 5 outlines the results. A comparative analysis was conducted with the methodology outlined in the research paper [40]. This approach is notable for its use of data-driven techniques, the application of system identification for model estimation, and the novel use of frequency gradient mapping for inertia extraction. The methodology emphasizes the importance of accurate inertia estimation in power systems with significant renewable energy penetration. Comparative analysis focuses on the methodologies and outcomes of both approaches. Despite the distinct approaches employed, the results presented in this paper align closely with the findings of the referenced study, especially in the context of inertia estimation in power systems heavily reliant on renewable energy sources. This correlation not only validates the presented approach but also reinforces the reliability of research outcomes. The consistency in results between the two studies indicates that the proposed approach yields satisfactory and comparably accurate outcomes in terms of inertia estimation within power systems. Thus, research contributes a robust and dependable method for inertia estimation in the evolving landscape of power systems, further establishing the potential and applicability of the approach in real-world scenarios. Additionally, the table also presents the error in percentages according to actual values of inertia. Figure 6 displays comparisons between actual and estimated inertia constant values. The technique successfully estimates the inertia constant for both simulated scenarios, one without RES penetration and the other with RES penetration. \begin{table}
\begin{tabular}{c c c c c} \hline
**RESs (\%)** & **H(s) Actual Value** & **H(s) Estimated** & **H(s) the Literature [40]** & **Error (\%)** \\ \hline
10 & 6.98 & 7.02 & 6.55 & 0.57 \\
15 & 6.05 & 6.37 & 6.44 & 5.29 \\
20 & 5.16 & 5.81 & 5.52 & 12.60 \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison of estimated inertia after disturbances with actual values and literature-based values at different renewable energy integration levels.
# Error Investigation on Wi-Fi RTT in Commercial Consumer Devices
## 4 Experimental Results and Analysis  
### Verification of the Effectiveness of the Blockers  
As illustrated in the experimental settings in Section 3.2, our calculation showed that the designed blockers should block the direct transmission path and attenuate the signal. To further verify the effectiveness of the blockers, we conducted a preliminary experiment by setting the smartphone and the AP at both sides and 5 m away from the blocker. We compared the RSSI values from the smartphone in LOS (no blocker) and NLOS (blocker with different materials) conditions. The mean RSSI is listed in Table 2. Note that the signal strength decreased in NLOS conditions with all blockers and smartphones. The results verify that the blocker's design and settings can effectively create an NLOS condition to attenuate the signal.
## 2 Design Issues of Drone Routing for Drone-Based Delivery Systems
### Priority-Aware Trajectory Design  
In a drone swarm delivering packages, it is important to consider the payload and battery constraints of the individual drones to minimize the overall delivery time. For example, drones with comparatively higher payloads will drain more battery than those with lower payloads. Therefore, some of the drones may consume their energy faster than usual, which would significantly affect the overall swarm performance. Thus, an ideal solution is to prioritize the drones carrying heavier payloads by designing an optimal trajectory to place them in a suitable position and to reduce the number of waypoints they visit [36]. This solution would ensure that these energy-constrained drones consume the least amount of energy by traversing through the optimal waypoints to reduce the overall delivery time, and the package delivery is guaranteed successfully without drone failure. In-flight drone-to-drone charging can also be a potential solution to recharge the drones with limited battery [36].
# Spatial Downscaling of Soil Moisture Based on Fusion Methods in Complex Terrains
## 2 Study Area and Data  
### Study Area  
The Tibetan Plateau, Sichuan Province of China (26.05-34.31\({}^{\circ}\)E, 97.35-108.54\({}^{\circ}\)N) is a typical \"mountain-basin\" area under the background of the large terrain of the Tibetan Plateau. It is located in the transition zone between the first level of the Tibetan Plateau and the second level of the Middle-Lower Yangtze plains, among the three major topographical steps of mainland China. As Figure 1 shows, there are three basic characteristics regarding the Sichuan topography: a high west and a low east undulating terrain, and complex and diverse landforms. The altitude difference between the east and west is greater than 7000 m. Plateaus, mountains, hills, plains, river valleys, rivers, and lakes are intertwined in this area, which is one of the most complex terrain areas worldwide. As shown in Figure 1a, the Huanyong Line divides Sichuan into two topographical areas: the western alpine plateau and southwestern mountains, the eastern Sichuan Basin and the basin edge mountains.  
Figure 1: Spatial distributions of (**a**) elevation, (**b**) slope, (**c**) aspect, and (**d**) land-cover type (LCT) in Sichuan. Black dots represent the locations of in situ soil moisture (SM).  
The climate of Sichuan is greatly influenced by the adjacent Tibetan Plateau. Owing to the effects of atmospheric circulation, monsoon advance and retreat, and other factors, the climate of Sichuan is complex. The complex and diverse topography causes the water and heat conditions to vary greatly, resulting in large regional differences in the soil and vegetation [48]. As shown in Figure 1d, the spatial distribution of land-cover types (LCTs) in Sichuan and different climatic zones lead to changes in vegetation. The topographic features also make the landscape having a significant vertical variation [49]. Under the action of gravity and hydraulic gradients, the soil is prone to large-scale erosion. The stratigraphic lithology is complex, neotectonic movement is intense, and disasters such as earthquakes, collapses, landslides, and debris flows are frequent [50]. Heavy rains are frequent in summer, which can easily lead to meteorological and mountain geological disasters. Disasters such as droughs in autumn, heavy rain, floods, and debris flows in summer are the main environmental problems faced by Sichuan, and SM is an important limiting factor for vegetation restoration [51].
## 4 Experimental Evaluations and Results
### Attributive Explanations with Beerl
For example, information about negative contribution of RSRP and SINR input features to the throughput reward component cannot be inferred from feature importance or reward decomposition methods alone. This information is important, especially when several input features have opposing contributions. With an existing method, a feature may not be described as important because the attribution value is small or zero. However, it may actually contribute to different components in an opposing way (e.g., high positive contribution to reward component 1 and high negative contribution to reward component 2), as shown by SINR input feature importances on Figure 8 (top plots, second row). By using our method, hidden information, as mentioned, can be revealed and the user may have a better understanding of the performing agent. Figure 6: Explanations generated using BEERL at the RL level where the respective reward prioritization (k; shown in parentheses) is applied to the every component. (a) Decomposed feature importance at the RL level. (b) Decomposed feature importance presented per reward component at the RL level. Figure 7: The normalized explanations where the importance of the input features and the reward components are compared in the same plot. (a) Normalized explanation at the Q-function level. (b) Normalized explanation when the reward weight is applied (at RL-level).
# An Exploration into Damage Repair and Manufacturing Technology of Photomask Glass Substrates
## 2 Equipment  
In this experiment, a three-dimensional white light scanning interferometer (Zygo, as shown in Figure 1) was used to measure the surface roughness of elements. In terms of this non-contact instrument for measuring the surface roughness of optical elements, the field of view was 0.07-9.3 mm, the longitudinal resolution was 0.1 nm, the transverse resolution was 0.36-9.50 \(\upmu\)m, and the measurement repeatability RMS was less than 0.01 nm [14].  
An ultra-smooth surface laser scattering quality detection device (ZC Optoelectronic Technologies Co., Ltd., ZYGO, Middlefield, CT, the United States of America, as shown in Figure 2) was used to characterize the distribution of damage points on the surface of damaged samples. This device can be employed to count and characterize the number of damage points of various sizes on the surface of samples [15].  
An array-type magnetorheological polishing device was independently developed by our laboratory, as shown in Figure 3. Under the action of the permanent magnet array arrangement principle, the original single magnetorheological polishing ribbon became double ribbons, which doubled the removal efficiency and saved processing time.  
Figure 1: White light interferometer.  
Figure 3: Array-type magnetorheological polishing device.  
Figure 2: Ultra-smooth surface laser scattering quality detection device.  
Mechanism of Magnetorheological Polishing Related to Subsurface Damage Removal and Mid-Frequency Ripple Error Control
## 2 Related Works
### Energy-Efficient Network Models  
Heterogeneous sensor networks organized into clusters employ a trustworthy energy-aware routing protocol [23]. The primary objective of this article is to reduce routing costs and increase network lifespan. The parameters of residual power, weight value for round-trip time (RTT), and hop count are used in the proposed routing strategy. However, reliable packet routing depends on CH election, and it requires a lot of power. To improve the network quality of service, the authors of [24] suggested an intelligent routing scheme. Limitations: Nearly 256 (4\({}^{\ddagger}\)) fuzzy rules are generated for the network, which requires very large energy. CNN is used to train the network according to conditions such as bandwidth availability, congestion status, and traffic level. Initially, K-means clustering is used to partition the nodes into clusters. The K-value is necessary for this purpose. To perform CH election in a heterogeneous WSN, genetic algorithm-based optimal clustering (GAOC) was presented [25]. The choice of the CH depends on many factors, including the total amount of energy remaining, its proximity to the sink nodes, and node density. Multiple information sinks are placed in the network to decrease the distance of communication between the nodes and the sink node, thereby mitigating the hot-spot issue. GA has been considered a powerful approach to CH election, whereas it does not guarantee the attainment of an optimum solution, just like other meta-heuristics. In [26], the matrix-filling theory was presented for data collection in an energy-efficient way. The main goal of this paper is to reduce latency, and the theory proposed is considered to meet this objective. It also uses cluster formation, and time slots are assigned to each cluster for information transfer; further, it is computed using the matrix-filling theory. However, the matrix-filling theory requires huge energy to fulfill all the operations. The energy coverage ratio clustering protocol (E-CRCP) was designed by the authors in [27] to exploit the regional coverage ratio in a way that decreases the node energy consumption. The CH is selected based on the node's area coverage, and the optimal number of clusters is determined according to the energy amount of each node. As a whole, this article helps the network to run more efficiently, distribute its load more evenly, and last longer. However, if the distance from the node is very high, high energy is needed. In such cases, interoperability fails.
# The Comparison of Electron Density between CSES In Situ and Ground-Based Observations in China
## 3 Comparison with ISR Data
Based on the two groups, the data were separately fitted in panel b of Figure 4, with the blue and black points denoting the data of group 1 and 2, respectively. The relationship of the Ne data for CSES and QJISR is better represented by a linear correlation in each group with the empirical formula in Equation (2). \[Y=19.94\times X-2.45\times 10^{11},Y=11.49\times X-4.3\times 10^{11} \tag{2}\]  
Furthermore, the correlation coefficient between the two datasets was calculated according to Equation (3). \[R(X,Y)=\frac{Cov(X,Y)}{\sqrt{Var[X]Var[Y]}} \tag{3}\]  
where \(X\) and \(Y\) represent the Ne values of the CSES and QJISR. _Cov_ means covariance between the two datasets, and _Var_ is the variance of each dataset. The correlation coefficients  
Figure 4: Ne scatters of CSES and QJISR with two groups (panel (**a**)) and the fitting lines (panel (**b**)). In panel (**a**), the number means the detailed date for each point with the format of YYMMDD. In panel (**a**), the data of groups 1 and 2 are represented by blue and black dots, respectively. Figure 3: Histogram for relative change of CSES Ne corresponding to QJISR Ne. The \(Y\) axis represents empirical cumulative distribution function (cdf). are 0.9250 and 0.8884 for the conjugate data of groups 1 and 2, respectively, which means the Ne of the CSES and QJISR exhibits a strong correlation.
# Fast Gaussian Filter Approximations Comparison on SIMD Computing Platforms
## 4 Approximations
### FIR Approximations
#### 4.1.3 Running Sums  
The running sums method, proposed by [PERSON] and [PERSON] in [12], is the approximation of Gaussian kernel based on the idea of computing a running sum of pixel values within a rectangular region. As in the case of Bell blur, running sums is a piecewise constant approximation but already with real rather than integer values. The fast computation uses the concept of integral images and cumulates the sum of pixel values along the image rows and columns.  
The authors defined \(k\) constant functions with values \(c_{i}\in\mathbb{R}\), \(i=1,\ldots,k\) and divided the segment \([-3\sigma;3\sigma]\) into the nested segments defined by the partition indices \([-p_{i},p_{i}]\), \(i=1,\ldots,k\), so the expression of the proposed approximation is  
\[y_{rs}[i]=\sum_{n=i-p_{k}}^{i+p_{k}}R[i-n]\,x[n], \tag{15}\]  
where  
\[R[n]=\begin{cases}c_{1},&\text{if }n\in[-p_{1};\,p_{1}],\\ c_{2},&\text{if }n\in[-p_{2};\,-p_{1})\cup(p_{1};\,p_{2}],\\ \vdots\\ c_{k},&\text{if }n\in[-p_{k};\,-p_{k-1})\cup(p_{k-1};\,p_{k}].\end{cases} \tag{16}\]  
The illustration is provided in Figure 3.  
An approach called integral images is used for fast computation of (15). It is proposed to compute and store the cumulative sums preliminarily  
\[I[i]=\sum_{n=0}^{i}x[n] \tag{17}\]for all pixels in an image row so that  
\[\sum_{n=i-p_{k}}^{i+p_{k}}x[n]=I[i+p_{k}]-I[i-p_{k}-1]. \tag{18}\]  
Let us set \(c_{k+1}=0\) and denote \(w_{i}=c_{i}-c_{i+1}\in\mathbb{R}\), \(i=1,\ldots,k\). Then, we can rewrite the expression (15) as follows:  
\[y_{rs}[i]=\sum_{n=1}^{k}w_{n}\big{(}I[i+p_{n}]-I[i-p_{n}-1]\big{)}, \tag{19}\]  
which means that the sum is accumulated by areas of horizontal rectangles, not vertical, as in (15).  
For given \(k\), the weights \(w_{1},\ldots,w_{k}\) and partition indices \(p_{1},\ldots,p_{k}\) are chosen to optimize the approximation of the Gaussian kernel. The authors provided the following values for cases with \(\sigma_{0}=\frac{100}{\pi}\) and \(k\in\{3,\,4,\,5\}\):  
\[k=3: p_{i}=\{23,46,76\}, w_{i}=\{0.9495,0.5502,0.1618\};\] \[k=4: p_{i}=\{19,37,56,82\}, w_{i}=\{0.9649,0.6700,0.3376,0.0976\}; \tag{20}\] \[k=5: p_{i}=\{16,30,44,61,85\}, w_{i}=\{0.9738,0.7596,0.5031,0.2534,0.0739\};\]  
and described how we can scale them for other \(\sigma\) values [12]:  
\[p_{i}^{(\sigma)}=\left\lfloor\frac{\sigma}{\sigma_{0}}\cdot p_{i}\right\rfloor,\qquad w_{i}^{(\sigma)}=\left\lfloor\frac{p_{i}}{2p_{i}^{(\sigma)}+1}\cdot w _{i}\right\rfloor. \tag{21}\]  
The number of arithmetic operations depends on \(k\). The method generally requires \(2k\) additions and \(k\) multiplications per image pixel. Note also that it requires extra memory. For storing the cumulative sum of a single row or column, the method requires \(O(\max\{h,w\})\) additional space over the input and the output images, where \(h\) and \(w\) are linear sizes of the image (height and width).