esa-sceva/satcom-chunk-collection huggingface dataset:
# SCMC: An Efficient Scheme for Minimizing Energy in WSNs Using a Set Cover Approach
## 3 Approach
### Cluster Creation  
Our main purpose is to extend the network lifetime as much as possible, and to minimize the energy consumption by keeping the amount of traffic to a minimum and by scheduling nodes to monitor a set of targets continuously in turn. To facilitate the achievement of our objective, a set of clusters is constructed. Many studies have adopted the creation of clusters in WSNs [34; 35; 36], however only some of them considered the use of a set cover algorithm. In this study, we consider the construction of a minimum set cover \(C=\{Ci\}\), \(1\leq i\leq K\) to create clusters. The problem can be described formally as follows.  
Set Cover Definition and Creation  
The set cover problem is one of [PERSON]'s 21 NP-complete problems shown to be NP-complete in 1972 [37].  
The set cover optimization has many applications in different areas [38] including manufacturing, service planning, location problems, network security, and multiple sequence alignments for computational biochemistry [39; 40; 41; 42].  
Given a set of elements (called the universe) and a collection \(S\) of \(m\) sets whose union equals the universe, the set cover problem is to identify the smallest sub-collection of \(S\) whose union equals the universe.  
In our paper we adopt the following approach: using a greedy set cover algorithm [19] to choose which cover a sensor node will belong to. In this approach, we define a finite set \(S=\{S_{1},\,S_{2},\,S_{3},\,\ldots,\,S_{m}\}\), corresponding to the areas to be monitored, a collection \(\{S_{j}\}n_{j}=1\) of subsets of \(S\), where each \(S_{j}\) represents a sensor and contains the areas that the sensor monitors from \(S\), and a positive integer \(k\geq 2\). The goal is to find a partition \(G\) of the subsets into \(k\) covers \(c_{1}\),..., \(c_{k}\) where each cover is a set of subsets, such that \(N(G)=k_{i=1}\,\mid\cup\,S_{j}\in c_{i}\,S_{j}\mid\) is minimized.  
The heuristic used in our paper consists of greedily picking sets from \(S\). The set \(S_{j}\in S\) that contains the most number of uncovered vertices is picked at each step. We repeat the process until all vertices are covered. The following is the pseudo-code of the greedy set cover algorithm.  
Once the set covers (clusters) are constructed, some sensor nodes become cluster heads (CHs) and collect all traffic from their respective cluster. The cluster head aggregates the collected data and then sends it to the base station as shown in Figure 3. The CH is chosen based on the highest residual energy, thus allowing the network lifetime to increase in proportion to node density [43].  
When using clustering, the workload on the cluster head is thus larger than for non-cluster heads. To maximize the lifetime of cluster heads, we propose to elect two cluster heads within each cluster that have the highest energy, and interchange their roles in a round-robin fashion during their lifetime in order to distribute and balance the extra workload and energy consumption evenly between them.
# A Heart Rate Variability-Based Paroxysmal Atrial Fibrillation Prediction System
## 3 Results
### The Converted Feature Matrices  
Each filtered HRV sequence was then converted into four extended Poincare plots. The four time lags, \(\tau=2\), \(1\), \(4\), and \(5\), obtained from GA were used for the conversions. With these time lags, two extended Poincare plots were accordingly generated from the original HRV signal with lags \(2\) and \(1\), and another two plots were generated from the mean-removed HRV signals with lags \(4\) and \(5\), respectively. Notably, the out-of-range data point and its corresponding lag point in the HRV signal were ignored during the conversion. With the matrix dimension \(s=32\) and the quantization factors \(q=4\) and \(5\), four Poincare plots of ranges [46; 174] \(\times\)[46; 174], [30; 190] \(\times\)[30; 190], \([-64,\,64]\)\(\times\)\([-64,\,64]\), and \([-80,\,80]\)\(\times\)\([-80,\,80]\) were discretized to four \(32\)\(\times\)\(32\) feature matrices. Since the binary vectors, **vm**, obtained from GA were all \(1\)s, all four matrices were retained for the inputs of the subsequent CNN. Figure 3 presents the images of the four matrices generated from a non-PAF subject in the upper panel, whereas those generated from a PAF patient are presented in the lower panel.  
Figure 3: Feature matrices input to the CNN. (**a**) Two inter-person feature matrices generated from the extended Poincaré plot of a non-PAF subject with \(\tau=2\), \(q=4\) and \(\tau=1\), \(q=5\). (**b**) Two Intra-person feature matrices generated from the extended Poincaré plot of a non-PAF subject with \(\tau=4\), \(q=4\) and \(\tau=5\), \(q=5\). (**c**) Two inter-person feature matrices generated from the extended Poincaré plot of a PAFpatient with \(\tau=2\), \(q=4\) and \(\tau=1\), \(q=5\). (**d**) Two intra-person feature matrices generated from the extended Poincare plot of a PAF subject with \(\tau=4\), \(q=4\) and \(\tau=5\), \(q=5\). All the matrix values were log-transformed for better visualization. The symbol \(\tau\) denotes time lag and \(q\) denotes the quantization factor.
## 5 Conclusions  
In this study, the translational mechanism of a Snapmaker 3D printer was modified to allow it to print cylindrical stents of different sizes and complexities on a metal rod. Then, according to the Taguchi methods, optimization experiments were performed on the printing parameters. The optimized parameters for printing stents included a flow rate of 100%, step size of 0.2 mm, and printing speed of 480 mm/min; moreover, printing pathway B was designated the optimal printing pathway. Furthermore, an enlarged stent was used to validate the Ansys finite element models, and after measuring the radial force and compression force, the simulation values were demonstrated to be similar to the experimental values. Therefore, the finite element model results were recognized to be able to represent actual circumstances. Next, the stent structure was designed to remain within 3% of the strain, allowing the stent to remain within the fully elastic deformation range to the greatest extent possible. The results successfully verified that when the stent was compressed into a catheter with a 2 mm diameter and expanded to a diameter of 6 mm, it could remain within the elastic deformation range. This demonstrated that the designed stent can provide the compliance and flexibility required to assist AVF maturation. In addition, this study successfully developed a novel AVF stent using TPU thread as the elastic bands, which can shrink the stent to 2 mm in diameter for delivery catheter placement and self-expand to 6.4 mm in diameter for AVF expansion. This also successfully verified that the self-expanding PLA stent can provide flexibility capabilities and sufficient radial force, such as a radial force of 0.08 N/mm, required by the AVF stent, and the possibility and feasibility of the degradable stent assisting AVF maturation.  
Conceptualization, F.-Y.C. and K.-J.H.; methodology, F.-Y.C. and T.-H.L.; software, K.-J.H. and T.-H.L.; validation, T.-H.L. and K.-J.H.; formal analysis, F.-Y.C., T.-H.L. and K.-J.H.; investigation, K.-J.H.; resources, F.-Y.C.; data curation, K.-J.H.; writing--original draft preparation, T.-H.L.; writing--review and editing, F.-Y.C.; visualization, T.-H.L. and F.-Y.C.; supervision, F.-Y.C.; project administration, F.-Y.C.; funding acquisition, F.-Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Ministry of Science and Technology, Taiwan, Republic of China under grant MOST 106-2221-E-011-071-MY2 and MOST 108-2221-E-011-128.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 4 Results and Discussion
### [PERSON] et al. (2015) Procedure  
The above consideration can also be extended to [PERSON] et al.'s [1] procedure. First, we used the same method as [PERSON] et al. [1] to determine how the drag coefficient varies in the flow direction. In particular, the \(c_{1}\), \(c_{2}\) and \(c_{3}\) values of Equation (18) that best represented  
Figure 3: Comparison between the calculated and observed \(C_{D}\) values using different predictors. (a) [PERSON] et al. [54], Equation (3); (b) [PERSON] and [PERSON] [61], Equation (14); (c) [PERSON] et al. [11], Equation (15); (d) [PERSON] et al. [63], Equation (16); (e) [PERSON] et al. [57], Equation (18).  
the experimental profile were determined for each test. Figure 4 shows for tests T5 and T18, the experimental points and the profiles obtained with Equation (18).  
Considering the calculated flow depths, it was possible to determine the EGL slope and, subsequently, the values of the drag coefficients using Equation (9) (here identified as \(C_{D-W}\)). Some results are shown in Figure 5.  
While in the experiments of [PERSON] et al. [1] the trends of \(C_{D}\) as a function of \(Re_{D*}\) were parabolic, in the present study, they were much more complex, showing in some cases a maximum and a minimum value, and in others increasing or decreasing linear trends. In particular:  
1. the M2 profiles that start from the critical depth have a bell-shaped pattern, at least on one side (T1, T3, T4, T17, T18, T24), and in some cases rather flat (T2, T19, T29). The difference, compared to the trends of [PERSON] et al. [1], is probably accredited to the fact that in those experiments the flume bed was horizontal;
2. when the bell is narrow, i.e., the variation in \(Re_{D*}\) is about 1000-2000, there is, on the left, an increase in \(C_{D}\) as \(Re_{D*}\) decreases (T7, T9, T10, T21, T22, T28);
3. when there is a hydraulic jump in the profile (M3-M2), the \(C_{D}\) value decreases as the \(Re_{D*}\) increases (T6, T16, T25);
4. if the test was performed by inserting the plate at the end of the channel (i.e., with a flow depth, downstream of the flow, higher than the critical one), the trend of \(C_{D}\) slightly increases with \(Re_{D*}\) (T5, T11, T20, T27) or remains almost constant (T23, T26).  
These conditions made it difficult to find a general law for \(C_{D}\) and encouraged us to try in a different way.
## 3 Background
### Propulsion System Technologies Overview  
A wide range of propulsion systems are available as off-the-shelf components, many of which have been adapted and miniaturized from existing larger scale satellite and rocket technologies. Typically, cold gas thrusters are selected as propulsion systems for CubeSats [23]; however, many developments have been made in this field due, in part to the increased popularity of the CubeSat program which can perform more complex maneuvers and provide higher values of specific impulse, for example pulsed plasma thrusters. Typically, thrusters are installed in larger bus satellites, but further miniaturization allows propulsion systems now to be considered for small craft such as 3U CubeSats [23]. Solutions such as Pulsed Plasma Thrusters and electrosprays are favored for CubeSats, as they are compact, easy to install and have a low degree of complexity. Technologies such as solar sails and Hall Effect thrusters which can provide increased range and increases in velocity for CubeSats are still under development. The advances of the solar sail are particularly of interest to the scientific world, as they could increase the duration of CubeSat mission indefinitely as solar radiation is perpetual power source in the realms of our solar system [8].  
CubeSats do not launch themselves. The P-POD carries the CubeSat outside Earth's atmosphere and into orbit. Once released into space the launch vehicle no longer controls them [1]. They are therefore required to be able to navigate self-sufficiently and maintain altitude. A CubeSat may also be required to have precise directional control depending on the satellites purpose [3]. For example, CubeSats are often deployed as inspectors for larger Satellites and are required to precisely navigate to allow the optimum angle for the integrated optics.
# Parameter Optimization Method for Metal Surface pBRDF Model Based on Improved Strawberry Algorithm
## 3 Improved Strawberry Optimization Algorithm
### Improved Algorithm
#### 3.2.1 Chaotic Mapping to Initialize the Mother Population  
Due to the use of random operators to initialize the position of the mother plant in the strawberry optimization algorithm, it is easy to encounter the problem of an uneven distribution of mother plant positions, weak global search ability, and low population diversity, causing a fall into local optima and affecting the overall optimization efficiency.  
In the field of optimization, chaotic mapping is usually used to replace pseudo-random number generators and generate chaotic sequences of [0, 1], which can simulate complex and random dynamic behaviors. Tent chaotic mapping has the characteristics of randomness and traversal. To ensure the distribution of the initial population is uniform and diverse, Tent chaotic mapping was introduced into SBA to initialize the population of the mother plants to maintain the population diversity when improving the global search ability of the algorithm. In this paper, The Tent chaotic mapping sequence is defined as follows:  
\[T_{n+1}=\left\{\begin{array}{ll}\frac{T_{n}}{u},&0\leq T_{n}<u\\ \frac{1-T_{n}}{1-u},u\leq T_{n}<1\end{array}\right. \tag{12}\]  
where \(n\) represents the number of populations, and \(u\) is a random number ranging [0, 1]. Combining with Tent chaotic mapping, the initial group position of the mother plants in the feasible domain is redefined:  
\[x_{i}=x_{lb}+(x_{ub}-x_{lb})T_{i} \tag{13}\]  
Figures 3 and 4 show the initial population of the mother plants generated by the random method and the introduced chaotic mapping, respectively.  
From Figures 3 and 4, it can be seen that the initial population distribution generated by Tent chaotic mapping is more uniform than that by the random method, which is more conducive to expanding the search range and increasing the diversity of population positions. To some extent, it can overcome the defect of SBA easily falling into local extremum and improve the convergence accuracy and optimization efficiency.  
Figure 3: The initial population generated by the random method.
# A Framework for Fall Detection Based on OpenPose Skeleton and LSTM/GRU Models
## 2 System Architecture and Method
### Model Architecture  
We chose RNN, LSTM and GRU models to learn with the joint points with time characteristics, because the RNN is suitable for dealing with problems that are highly related to time series. At the same time, it does not need to be calculated as a full image detection method, as using the entire picture increases training time and hardware burden. Moreover, the human joints are connected, so the posture will have a certain rule in the change of time \(t\).
# Design and Application of Logical Range Framework Based on Digital Twin
## 3 Key Technologies
### Data Communication Architecture  
In the flight test, multiple ranges and various equipment are required to carry out coordinated tests. Therefore, a large amount of real-time data will be generated during testing. The system needs to receive, process, calculate, and display these large amounts of real-time data, which poses huge challenges regarding the data-processing ability and network performance of the system. In order to reduce the processing pressure and network burden of the system, the data are divided into static data and dynamic data, as shown in Figure 5. Dynamic data must be generated, received, and acquired in real-time during system operations, whereas static data only need to be acquired once (e.g., during system startup). This can reduce the system load to a certain extent.  
Dynamic data are the real-time data collected by each piece of equipment and sensor during the operation of the Logical Range system based on digital twin technology. Dynamic data mainly consist of two parts: the data collected by sensors (e.g., current humidity, temperature, wind speed, wind direction, and other environmental information), and the data collected and generated by each piece of equipment (e.g., the latitude and longitude, magnetic declination angle, signal-to-noise ratio, radial velocity, height, acceleration, angular acceleration, position coordinates, horizontal angle, vertical angle, semi-measured back-angle, and so on).  
Figure 4: Equipment addition process flowchart.  
The static data are mainly the information of the physical equipment in the range, such as the basic parameters of the equipment, the geometric parameters of the equipment, the basic working parameters of the equipment, and so on. Static data are generally the basic information required for the normal operation of the equipment, which can typically be obtained from the equipment manual or the equipment use staff.  
In order to realize the data collection described above, we designed the Logical Range system based on digital twin communication architecture, as shown in Figure 6. Each piece of equipment is connected to the associated equipment proxy running on the range side through a short-distance transmission protocol. The equipment proxy obtains the data from the equipment, performs simple checksum processing on the data, and stores the processed data in the local database of the range. The equipment proxy stores the data in the local database and sends the data to the cloud at the same time, storing the data in the cloud real-time database. The data are then sent to the message middleware, in order to drive the functions related to the virtual and service layers.  
The Logical Range system based on digital twin technology, as the data reading client, reads the real-time data in a subscribe/publishment to realize the real-time synchronous display of virtual and real data. At the same time, the data stored in the cloud also provide data support for data analysis, data calculation, and other functions of the service layer.  
Figure 5: Data classification.
## 4 Results
### Regression Analysis
#### 4.1.3 Model Performance and Statistical Significance  
The regression model demonstrated a robust explanatory power, with an R-squared value of 0.9647 and an adjusted R-squared of 0.9614. These metrics indicate that approximately 96.47% of the variability in the Box-Cox-transformed suspended sediment concentration is accounted for by the model. The F-statistic of 296.53 (Prob (F-statistic) = 1.83 \(\times\) 10\({}^{-52}\)) underscores the overall statistical significance of the regression model.  
The analysis identified several statistically significant variables (\(p\)-value < 0.05), including the following:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Term** & **Coefficient** & **Std. Error** & **t-Value** & \(p\)**-Value** \\ \hline
**const** & **7.7971** & **0.2673** & **29.1676** & **0.0** \\
**Fr** & **5.1125** & **0.2045** & **24.9989** & **0.0** \\
**Sq** & **1.095** & **0.1577** & **6.9429** & **0.0** \\
**t\_sq** & **-0.7915** & **0.2045** & **-3.8712** & **0.0002** \\
**Obj\_T** & **-1.0589** & **0.3822** & **-2.7706** & **0.007** \\
**Fr\_Obj\_T** & **-0.7154** & **0.2952** & **-2.4233** & **0.0178** \\
**Sq\_Obj\_T** & 0.124 & 0.2504 & 0.4952 & 0.6219 \\
**t\_sq\_Obj\_T** & \(-0.0837\) & 0.2961 & \(-0.2826\) & 0.7783 \\ \hline \hline \end{tabular} Note: Statistically significant terms at the 0.05 level are highlighted in bold.  
\end{table}
Table 3: Summary of regression coefficients and statistical significance.  
* Froude number (\(Fr\)),
* Squat ratio (\(Sq\)),
* Squared time (\(t^{2}\)),
* Floating object type (\(Obj\_T\)),
* Interaction between \(Fr\) and \(Obj\_T\) (\(Fr\times Obj\_T\)).  
These significant terms underscore the complex interplay between operational parameters and floating object characteristics in influencing sediment resuspension.  
The residuals of the model were examined to assess the assumptions of normality. Figure 24 presents a normal Q-Q plot of these residuals, which, upon careful analysis, reveals a distribution closely approximating normality. This conformity to the expected pattern provides significant support for the fundamental assumptions of the regression model and increases confidence in its validity.
# Flexible Simulation Platform for Generating Realistic Waveforms with Voltage Notches
## 5 Comparison of Simulations with Field Measurements
### Simulation Setup and Waveform Comparison
These graphs focus on the period immediately surrounding the voltage notch, allowing for a precise comparison of notch characteristics between field measurements and simulations. Figure 15: Case study from field measurements. The overlaid waveforms demonstrate a high degree of similarity in terms of notch depth, width, and shape across all three phases. Some slight differences are observed in the POW, probably due to a certain level of unbalance in the field measurement record. Similar characteristics in commutation oscillation patterns are observed in phase \(c\), whereas phases \(a\) and \(b\) show slight differences, suggesting a possible unbalance in network impedance. Figure 16: Example of three-phase voltage (**top**) and current (**bottom**) waveforms from a field measurement record (**left**) and a simulation (**right**). Figure 17: Overlaid voltage notch (**top**) and current (**bottom**) waveforms from the example of the field measurement record and simulation in Figure 16 for visual comparison. Phase \(a\) (**left**), phase \(b\) (**middle**), and phase \(c\) (**right**). The bottom row in Figure 17 shows the corresponding current waveforms for each phase over the period surrounding the voltage notch. These graphs allow for a comprehensive comparison of current behavior, including the shape of the waveform, peak values, and timing of current pulses. The simulated current waveforms show good agreement with field measurements in terms of pulse timing and general shape. However, some minor differences in POW and commutation oscillations are noticeable. Overall, Figure 17 provides visual evidence of the simulation platform's capability to generate synthetic waveforms that closely resemble real-world measurements. This comparison supports the validity of the simulation approach for studying voltage notches. While the simulation approach involves varying parameters that are typically constant in a real-world single installation, this methodology was adopted due to the limited availability of detailed installation data. The primary goal was to demonstrate the simulation platform's capability to replicate the observed voltage notch phenomena under plausible operating conditions. It is acknowledged that this introduces limitations to the validation. Future work should aim to include measurements from multiple installations with known parameters to provide a more robust validation of the simulation platform.